HK1221141B - Planning, navigation and simulation systems and methods for minimally invasive therapy - Google Patents

Description

Planning, navigation and simulation systems and methods for minimally invasive therapy

Cross-references to Related Patent Applications

This application claims priority to U.S. Provisional Patent Application No. 61/800,155, entitled “PLANNING, NAVIGATION AND SIMULATION SYSTEMS AND METHODS FOR MINIMALLY INVASIVE THERAPY,” filed on March 15, 2013, the entire contents of which are incorporated herein by reference.

This application also claims priority to U.S. Provisional Patent Application No. 61/924,993, entitled “PLANNING, NAVIGATION AND SIMULATION SYSTEMS AND METHODS FOR MINIMALLY INVASIVE THERAPY,” filed on January 8, 2014, the entire contents of which are incorporated herein by reference.

This application also claims priority to U.S. Provisional Patent Application No. 61/845,256, entitled “SURGICAL TRAINING AND IMAGING BRAIN PHANTOM,” filed on July 11, 2013, the entire contents of which are incorporated herein by reference.

This application also claims priority to U.S. Provisional Patent Application No. 61/900,122, entitled “SURGICAL TRAINING AND IMAGING BRAIN PHANTOM,” filed on November 5, 2013, the entire contents of which are incorporated herein by reference.

Technical Field

The present disclosure relates to planning, navigation, and simulation systems and methods for minimally invasive therapies.

Background Art

In medicine, imaging and image guidance are becoming an essential part of clinical care. From diagnosing and monitoring disease to planning surgical approaches, and guidance during and after the procedure, imaging and image guidance offer effective and versatile treatment options for a wide range of procedures, including surgery and radiation therapy.

Targeted stem cell delivery, adaptive chemotherapy regimens, and radiation therapy are just a few examples of procedures that use imaging guidance in medicine.

Advanced imaging modalities such as magnetic resonance imaging ("MRI") have led to improved detection, diagnosis, and staging rates and accuracy in several medical fields, including neurology, where diseases such as brain cancer, stroke, intracerebral hemorrhage ("ICH"), and neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease are imaged. As an imaging modality, MRI is often capable of three-dimensional visualization of tissue in soft tissue with high contrast without the use of ionizing radiation. The modality is often used in combination with other modalities such as ultrasound ("US"), positron emission tomography ("PET"), and computed tomography ("CT") by examining the same tissue using different physical principles available with each modality.

When used in conjunction with intravascular agents such as iodinated contrast agents, CT is often used to visualize bone structures and blood vessels. MRI can also be performed using similar contrast agents such as intravascular gadolinium-based contrast agents, which have pharmacokinetic properties that enable visualization of tumors and breakdown of the blood-brain barrier.

These multimodal solutions can provide varying degrees of contrast between different tissue types, tissue functions, and disease states. Each imaging modality can be used in isolation or in combination to better differentiate and diagnose disease.

For example, in neurosurgery, brain tumors are often removed through an open craniotomy guided by imaging. The data collected in these solutions typically consists of CT scans using associated contrast agents such as iodinated contrast agents and MRI scans using associated contrast agents such as gadolinium contrast agents. In addition, optical imaging is often used in the form of a microscope to distinguish the boundary between the tumor and healthy tissue, which is called the periphery. Tracking of the instrument relative to the patient and the associated imaging data is often achieved with the help of external hardware systems such as robotic arms or radiofrequency or optical tracking devices. As a group, these devices are often referred to as surgical navigation systems.

Previously known systems for multimodal imaging for planning and navigation include the integration of imaging data from the surgical suite in the operating room. Technology has allowed three-dimensional modalities including PET, CT, MRI, 3D ultrasound and two-dimensional modalities such as X-ray and ultrasound to be viewed together to form an image set used in the operating room. These image sets can be used to help surgeons better resect diseased tissue such as cancer, to guide the repair of vascular defects such as stroke and ICH, to deliver treatments for psychiatric conditions such as major depressive disorder or obsessive-compulsive disorder, to perform procedures such as deep brain stimulation ("DBS") for Parkinson's disease, Alzheimer's disease, and Huntington's disease, and to guide radiation oncologists in radiation therapy for brain tumors.

These solutions have attempted to integrate different imaging modalities into the surgical suite by using intraoperative imaging, such as by registering and tracking real-time ultrasound images; by using a "C"-arm ("C-arm") for X-ray or CT imaging, for example, by using a dedicated MRI system for a specific part of the anatomy (e.g., the head); and by using a mobile MRI system. Generally speaking, these systems have not fully exploited the ability to achieve better imaging with the improved access afforded by the surgical procedure itself, nor has the acquired information been integrated into the procedure in a way that addresses fundamental challenges associated with disease management.

Therefore, there is a need for a multimodal imaging system and method that enables surgical planning and navigation by analyzing input retrieved from improved tissue pathways resulting from the surgical procedure itself.

Furthermore, there is a need to effectively record registered or integrated images and other inputs in a meaningful manner. Additionally, there is a need to integrate other valuable data points related to the physics of the surgical tools or the tissue itself. Therefore, there is a need for multimodal imaging systems and methods that enable surgical planning and navigation by meaningfully integrating multiple data points retrieved during, before, and after surgery to provide improved surgical and navigation systems. There is also a need for systems and methods that utilize surgical procedure and tool-specific information to provide improved navigation and planning.

In addition, imaging in existing solutions is often performed on large portions of tissue (such as brain tissue) that are accessed by open surgical methods that are highly invasive to the patient. There is also an increasing class of procedures, including neurosurgical procedures, that ideally would require only minimally invasive navigation and imaging system methods. For example, ICH repair, stroke repair, deep brain tumor surgery, intraaxial brain tumor surgery, intranasal surgery such as pituitary or brainstem surgery, stem cell therapy, targeted drug delivery, and deep brain stimulator delivery are all examples of procedures that are very suitable for minimally invasive methods. Many surgical methods in neurosurgery have become more dependent on minimally invasive methods to remove diseased tissue, modify vascular and coagulation problems, and maintain as much healthy neural tissue as possible. However, existing intraoperative surgical systems such as navigation and imaging solutions are often insufficient. Although methods for removing tissue through intranasal approaches, methods based on access ports, and positioning of electrical stimulation devices have become important procedures, medical imaging and navigation programs have not yet evolved to accommodate the specific needs of these methods.

Therefore, there is a need for a multimodal imaging system and method for achieving surgical planning and navigation through minimally invasive means and methods.

Additionally, because port-based procedures are relatively new, the detailed application of imaging to such procedures has not been anticipated, nor have the effects of interfaces between known devices on tissue been integrated into planning systems. During craniotomy, the complexity of the multiple contrast mechanisms used in known systems can overwhelm software system architectures. Furthermore, complex issues associated with tissue migration that occurs during surgery have not been well addressed. Therefore, there is a need for a system and method for preoperative and intraoperative planning and navigation to enable minimally invasive port-based surgical procedures and larger open craniotomies.

In existing systems, a radiologist, neurologist, surgeon, or other medical professional typically selects an imaging volume based on diagnostic imaging information or clinical information about the patient. This imaging volume is often associated with a proposed trajectory for performing the surgery (e.g., the path for inserting a needle). However, one drawback of existing systems is that this information about the tumor location and trajectory cannot typically be modified or interacted with in the surgical suite, which results in limited use of this detailed information when additional information arises during surgery (e.g., the location of a blood vessel or critical structure that conflicts with the preselected trajectory). Therefore, a system is needed that provides real-time surgical procedure planning corrections.

Summary of the Invention

The present invention relates to a planning system for minimally invasive therapy. The present invention provides a system and method for planning a path to a target location in tissue within a patient's body. The system comprises a storage medium storing a preoperative imaging volume and surgical outcome criteria associated with an anatomical part of the body; and a processor in communication with the storage medium and the outcome criteria to identify, save, and score one or more surgical trajectory paths.

In one embodiment, the system includes a memory device, a computer processor, and the like that cooperate to receive, store, and calculate inputs and surgical trajectory paths and display the results on a user interface.

In another embodiment, a computer-implemented method for planning access locations to tissue within a patient's body is disclosed. The method includes the following steps: receiving input via a user interface of a computer; generating a 3D image including entry points to the tissue; calculating and storing one or more surgical trajectory paths based on surgical outcome criteria; and displaying a selected trajectory path on the user interface.

Also disclosed is a system for planning brain surgery. The system includes a storage device for storing at least one preoperative 3D imaging volume, and a computer processor that receives input (i.e., a sulcal entry point, a target location, surgical outcome criteria, and the 3D imaging volume), calculates a score based on the surgical outcome criteria, and displays one or more trajectory paths based on the score.

In another embodiment, a system for planning access locations to tissue within a patient's body is disclosed. The system includes a storage medium, a display, a user interface, and a computer program having a plurality of code segments configured to generate a 3D image, receive user input, calculate one or more point-by-point trajectory paths associated with surgical outcome criteria, and assign associated scores to the one or more trajectory paths.

In another embodiment, a system for planning access locations to tissue within a patient's body is disclosed. The system includes a storage medium, a display, a user interface, and a computer program having a plurality of code segments configured to generate a 3D static or animated image, receive user input, store a preoperative imaging volume, calculate one or more point-by-point trajectory paths relative to known points in the imaging volume that are associated with surgical outcome criteria, assign scores to the one or more trajectory paths, and derive the one or more such paths.

A further understanding of the functionality and advantages of the present invention may be realized by referring to the following detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments disclosed herein will be more fully understood from the following detailed description thereof when taken in conjunction with the accompanying drawings, which form a part of this application and in which:

FIG1 is a block diagram showing the system components and inputs used to plan and score the surgical pathways disclosed herein.

2 is a block diagram showing system components and inputs for navigating along a surgical path generated by the planning system of FIG. 1 .

FIG3 is a block diagram showing the system components and inputs used for postoperative data analysis.

4A and 4B illustrate an embodiment of the method and system of the present invention in which a processor identifies fiber bundles to facilitate optimal selection of a surgical approach.

FIG5 is a flow chart illustrating the processing steps involved in the planning system and method disclosed herein.

FIG6 illustrates an exemplary, non-limiting implementation of a computer control system for implementing the planning and guidance methods and systems disclosed herein.

Figure 7 illustrates the output of an embodiment of the method and system of the present invention, showing a visualized patient anatomy using three orthogonal projections. The two display panes in the top row and the leftmost pane in the bottom row show 2D projections that are orthogonal to each other.

FIG8 illustrates a diagram highlighting the tracts predicted to be intersected by the surgical tool for the indicated pose or orientation relative to 2D patient data.

FIG9 shows a graph of the same patient data as shown in FIG8 , but with different bundles intersected by the surgical tool for different poses relative to the target in the brain.

FIG10 shows a visualization of the extent of the craniotomy using the selected trajectory and surgical tools, and illustrates the space available for manipulating the surgical tools during surgery.

DETAILED DESCRIPTION

Various embodiments and aspects of the present disclosure will be described with reference to the details discussed below. The following description and accompanying drawings are intended to illustrate the present disclosure and should not be construed as limiting the present disclosure. Numerous specific details are described to provide a thorough understanding of the various embodiments of the present disclosure. However, in some instances, well-known or conventional details are not described in order to provide a concise discussion of the embodiments of the present disclosure.

As used herein, the terms "comprises" and "comprising" should be understood as inclusive and broadly construed, rather than exclusive. Specifically, when used in the specification and claims, the terms "comprises" and "comprising" and variations thereof mean that the specified features, steps, or components are included. These terms should not be interpreted to exclude the presence of other features, steps, or components.

As used herein, the term "exemplary" means "serving as an example, instance, or illustration," and should not be construed as preferred or advantageous over other configurations disclosed herein.

As used herein, the terms "about" and "approximately" are meant to encompass possible variations within the upper and lower limits of a range of values, such as variations in properties, parameters, and dimensions.

As used herein, the term "patient" is not limited to a human patient and may refer to any living organism to be treated using the planning and navigation system disclosed herein.

As used herein, the phrase "surgical tool" or "surgical instrument" refers to any item that can be guided along a path within a patient's body to a site. Examples of surgical tools may include, but are not necessarily limited to, scalpels, resection devices, imaging probes, sampling probes, catheters, or any other device that can access a target location within a patient's body (or facilitate access by another surgical tool to a location within a patient's body), whether diagnostic or therapeutic in nature.

As used herein, the phrase "optical coherence tomography" or "OCT" refers to an optical signal acquisition and processing method that captures micron-resolution, three-dimensional images from within an optically scattering medium such as biological tissue. OCT is an interferometric measurement technique that typically uses near-infrared light. The use of relatively long wavelengths allows the light to penetrate into the scattering medium. In the context of medical imaging, the advantage of OCT is that it provides images of tissue morphology with much higher resolution (greater than 10 μm), which is currently superior to other imaging modalities such as MRI or ultrasound. However, OCT is currently limited to imaging 1 to 2 mm below the surface in typical biological tissue, and at greater depths, the proportion of light that escapes without scattering is too small to be detected. Images can be obtained "contactlessly" or through a transparent window or membrane that must be in line of sight with the target tissue.

As used herein, the phrase "polarization-sensitive optical coherence tomography (PS-OCT)" refers to an imaging technique that provides depth-resolved measurements of the polarization state of light reflected from a turbid medium, such as tissue. Measurement of the depth-resolved Stokes parameters allows determination of the degree of polarization and optical axis orientation in a turbid medium that can be modeled as a linear retarder.

As used herein, the term "ultrasound" or "US" refers to an imaging technique that uses sound waves in the frequency range of approximately 2 to 18 MHz. The selected frequency for a particular medical imaging procedure is often a trade-off between the spatial resolution of the image and the depth of imaging penetration. Lower frequencies produce lower resolution but can image deeper into the body, while higher frequency sound waves produce higher resolution (due to the smaller wavelength and, therefore, the ability to reflect or scatter from smaller structures). Higher frequency waves also have a larger attenuation coefficient and are therefore more easily absorbed in tissue, thereby limiting the depth of penetration of the sound waves into the body.

As used herein, the phrase "positron emission tomography" or "PET" refers to a nuclear medicine imaging technique designed to produce three-dimensional images of functional processes in the body. A PET system works by detecting pairs of gamma rays emitted by positron-emitting radionuclides, or tracers, injected into the body. Computer analysis then constructs a three-dimensional image of the tracer concentration within the body.

As used herein, the phrase "computed tomography" or "CT" (also known as "X-ray computed tomography" or "x-ray CT") refers to a technique that uses computerized x-rays to produce tomographic images (virtual "slices") of specific areas of a scanned object. A three-dimensional image of the interior of the object being studied can be generated from a series of two-dimensional radiographic images taken around a single axis of rotation using the techniques of digital geometry processing. CT scans of the head/brain are commonly used to detect, among other things, hemorrhages, bone lesions, tumors, infarctions, and calcifications. Among these, low-density (dark) structures are generally indicative of edema and infarctions, while high-density (bright) structures are generally indicative of calcifications and hemorrhages. Tumors can often be detected by swelling and the anatomical deformation it causes, or by any surrounding edema.

As used herein, the phrase "magnetic resonance imaging" or "MRI" refers to a medical imaging technique used in radiology to visualize the internal structures of the body, and is used to study both healthy and diseased anatomical structures and functions. MRI is the research tool of choice for neurological cancer because it is more sensitive to small tumors than CT. In addition, for many conditions of the central nervous system, including but not limited to demyelinating diseases, the contrast between the gray and white matter of the brain provided by MRI makes it the first choice. In addition, specialized MRI pulse sequences can be used to provide different types of information. For example, "diffusion MRI" is an MRI sequence that measures the diffusion of water molecules in biological tissues and is clinically useful for diagnosing conditions such as stroke or neurological diseases such as multiple sclerosis, and is particularly useful for understanding and visualizing the directionality and connectivity of white matter tracts in the brain. Examples of diffusion MRI are diffusion tensor imaging ("DTI") and diffusion-weighted imaging ("DWI"). In addition, "functional MRI" or "fMRI" is another specialized MRI sequence that is sensitive to changes in blood oxygen levels and can be used to infer areas of increased cortical activity. Typically, with fMRI, patients are asked to perform a specific task (e.g., motor activity, cognitive exercises), and the highlighted areas in the fMRI scan can indicate which areas of the brain have increased blood flow (and are therefore more active) when performing such a task.

MRI can also be performed as a perfusion scan, which incorporates the use of a contrast agent (usually gadolinium) and observes how such a contrast agent moves through tissue over time. A typical perfusion scan technique first acquires a baseline 3D volume, injects the contrast agent, and then performs repeated scans (with the patient remaining in the same scanning position during the scanning session).

In the three examples of MRI techniques mentioned above (diffusion MRI, fMRI, perfusion MRI), what is generated is a 4D dataset (i.e., a 3D volume evolving over time) that includes, in addition to static imaging data, data related to water diffusion (diffusion MRI), blood oxygenation (fMRI), or contrast agent moving through tissue (perfusion MRI).

In some embodiments, the systems and methods may include the use of tractography. In the systems and methods described herein, differentiation between tumors and healthy tissue can be performed using a DWI sensor and an associated processor that uses the diffusion of water through brain tissue by Brownian motion as the primary tissue contrast mechanism. Data collected from a diffusion contrast scan can be collected with predetermined gradient directions so that diffusion can be visualized along specific directions in the brain. This directional information can be used to generate a connection map defined by sets of vectors to generate fiber tracts in the brain; wherein these fiber tracts correspond to water diffusion through the brain on the outside of the white matter tracts and correspond to the major nerve fibers in the brain.

The different imaging modalities mentioned above can be combined to provide a deeper understanding and more information that can be obtained using only one modality. For example, a PET scan can be performed in conjunction with a CT and/or MRI scan, where the combined image (called a "co-registered" image) provides better information and can include both anatomical and metabolic information. For example, because PET imaging is most useful in conjunction with anatomical imaging such as CT, modern PET scanners often include integrated high-end multi-slice spiral CT scanners (so-called "PET/CT"). In these machines, the two types of scans can be performed in the same session in a side-by-side sequence, with the patient not changing position between the two types of scans, so that the two sets of images are more accurately co-registered, so that abnormal areas observed using the PET imaging modality can be more accurately associated with anatomical structures observed from the CT images. This is very useful in showing detailed views of moving organs or structures with greater anatomical deviations (which are more common outside the brain).

Thus, as used herein, the phrases "registration" or "co-registration" refer to the process of transforming different sets of data into a single coordinate system, and "image registration" refers to the process of transforming different sets of imaging data into a single coordinate system. The data can be multiple photos, data from different sensors, time, depth, or viewpoints. In this application, the process of "co-registration" relates to medical imaging, in which images from different imaging modalities are co-registered. Co-registration is necessary to enable comparison or integration of data obtained from these different modalities. Those skilled in the art will appreciate that there are many available image co-registration techniques, and one or more of these techniques can be used in this application. Non-limiting examples include intensity-based methods, which compare intensity patterns in images using correlation measures, and feature-based methods, which find correspondences between image features such as points, lines, and contours. Image registration algorithms can also be categorized based on the transformation model used by the algorithm to relate the target image space to the reference image space. Another classification can be between single-modality methods and multi-modality methods. Single-modality methods typically register images acquired in the same modality by the same scanner/sensor type, while multimodality registration methods are used to register images acquired by different scanner/sensor types. In the present disclosure, multimodality registration methods are used in medical imaging of the head/brain, where images of a subject are acquired from different scanners. Examples include co-registration of brain CT/MRI or PET/CT images for tumor localization, registration of contrast-enhanced CT images to non-contrast-enhanced CT images, and registration of ultrasound to CT.

It should be understood that the planning and navigation methods and systems disclosed herein are applicable to imaging modalities that are not currently available. For example, with reference to MRI, new sequences, methods, or techniques beyond those outlined herein may be further available biomedical imaging information that can be readily incorporated into the methods and systems disclosed herein through appropriate co-registration techniques.

As used herein, the phrase "preoperative imaging modality" refers to the modalities herein and any other imaging technique that has the necessary tissue penetration to image the anatomy prior to commencing an invasive procedure.

As used herein, the phrase "surgical outcome criteria" refers to the clinical goals and expected outcomes of a surgical procedure as envisioned by a surgeon trained in such a procedure. Typically, the surgical intent of brain tumor resection surgery is to remove as much of the tumor as possible while minimizing trauma to the remainder of the brain and surrounding tissue structures (surrounding tissue structures, in this context, include any tissue structures that are directly or indirectly affected during the surgical procedure). Examples of tissue structures surrounding the brain include, but are not limited to, the dura mater, cerebrospinal fluid, and skull.

As used herein, the phrase "point-by-point surgical trajectory path" means any continuous (i.e., uninterrupted) line representing a path passing through a starting point (also referred to as an entry point), a succession of multiple waypoints, and an end point representing a target, wherein each point is connected to its adjacent points by a curve or straight line defined in 3D space; the path is a representation of a surgical trajectory used to accomplish one or more surgical outcome criteria.

As used herein, the phrase "waypoint" means a point formed between the start and end points of a point-by-point surgical trajectory path that needs to be traversed using these points in a sequence determined by the surgeon to meet the surgical intent. In many cases, waypoints are points that are formed to guide a point-by-point surgical trajectory path along a desired trajectory. However, waypoints can also indicate points on a trajectory where specific surgical actions can be performed. For example, a waypoint can be introduced along a trajectory used in brain surgery to alert the surgical team that a biopsy specimen may have to be obtained. Alternatively, waypoints can be used to send a message to the navigation system that a parameter may have to be changed. For example, it may be advantageous to have an external video scope switch (automatically or after user confirmation) from a wide field of view during a craniotomy to a narrow field of view during dura opening.

As used herein, the phrase "3D image" refers to the display of an image that contains spatial information in more than two dimensions. Such displays include, but are not limited to, stereoscopic displays, dynamic computer models with interfaces that allow rotation and depth selection, perspective views, and holographic displays. In addition, it is well known in the art that a 3D image can be represented by a series of 2D images with varying depths or angles, and therefore, reference to a "3D image" is similar to a reference to a set of different 2D images of the same object. A 3D image can also be formed directly from 3D measurements in some modalities (e.g., MRI), so that a series of 2D images is not normative. Furthermore, the terms "volume" and "image" are used interchangeably in this context.

As used herein, the phrase "code segment" refers to a unit of code, such as an algorithm or program, that can be executed on a computer. Embodiments of the present disclosure may include multiple code segments. Code segments are labeled with ordinal numbers, i.e., "first code segment," "second code segment." It should be understood that ordinal numbers do not indicate a specific order in which the code must be executed or implemented, nor do they imply interdependencies among programs or algorithms.

While the methods and systems of the present invention can be used to perform surgical procedures on any part of a patient's anatomy, they are particularly useful for performing brain surgical procedures because they advantageously utilize imaging information that displays the inferred position and orientation of small nerve bundles and major nerve fiber bundles in the brain. The embodiments described herein are configured to provide systems and methods that detect and suggest surgical corridors to regions of the brain for a given procedure and predict the potential impact of the approach on healthy brain tissue. Such impact assessments allow surgeons to make decisions about the approach using quantitative assessments.

For example, there is currently no clinically acceptable means to perform biomechanical modeling of brain movement for minimally invasive access surgery. Existing systems are generally unable to determine the potential movement of brain tissue during surgery to suggest a modified approach to the lesion; to suggest a modified surgical approach that will allow more diseased tissue to be removed while leaving more healthy tissue unaffected; and to evaluate the impact on the brain and tissue shifts caused by tissue resection before actual resection (since most existing scans and surgical models are contained in a solid container, the shifts in the matrix material are generally minimal). In addition, there is no way to utilize multiple imaging contrast data sets that provide appropriate biomechanical information to perform the imaging registration required to update preoperative planning. In other embodiments, means for managing total brain displacement, for example, by means of a small craniotomy access hole and using the brain's natural cavities allow the use of simulation methods in such a way that surgical approaches can be conveyed in ways that are not possible with existing solutions.

Furthermore, there are no existing planning and training systems that can be used to plan and navigate through brain sulci. Thus, there is a need for surgical planning and training systems and methods for planning trajectories along pathways (e.g., along brain sulci) because existing molds used for surgical models often do not mimic the ridge structures present on the surface of the brain.

Additionally, existing training systems are generally inherently unable to fine-tune a training session to a specific surgical scenario, as known training is performed using an agar gel enclosed in a square mold with a grape located at the center of the cube near the bottom, which does not provide the surgeon with a clear understanding of constraints such as inhomogeneity, orientation, and tissue displacement under gravity and pressure. Other embodiments provide intelligent systems and methods that allow for practicing an entire surgical procedure on a simulation platform, which may be useful for performing a simulated surgical procedure at least one day in advance of the surgical procedure to identify the patient's head orientation, thereby identifying and placing appropriate surgical tools in advance of the procedure. This can be achieved by using a surgical model that accurately mimics the brain dimensions of a specific patient and the position of a tumor model at a geometrically accurate location within the brain model.

The ability to image nerves and establish surgical procedures to guide devices or remove tissue while avoiding these nerves requires the integration of navigation technology, software planning systems, preoperative imaging, and surgical tools. Embodiments of the described systems and methods are used to provide an interface from which surgeons can plan minimally invasive approaches based on the most recent imaging available for the patient. In the context of white matter tracts in the brain, because neural tracts represent complex datasets that can best be represented in a three-dimensional context, this information can be critical to accurately representing surgical approaches provided by existing systems and methods in order to provide the most likely route to the target of interest. Representing surgical tools and surgical approaches relative to these white matter tracts and the target of interest (often complex tumor geometries) has not been addressed in a manner that enables efficient trajectory planning for the surgical approach. Furthermore, the path used for access is often selected to minimize the amount of gray and white matter traversed, without carefully considering the object to which the white matter is attached (the cortical bank of gray matter) or the state of the white or gray matter, i.e., whether it has a chance of recovery or is functional. Furthermore, the use of natural access pathways in the brain has not been considered in the context of planning.

For example, the natural folds of the brain (i.e., sulci) provide ideal minimally invasive access pathways to deep locations in the brain. To effectively utilize these pathways, a novel software planning system and method is provided for processing and calculating input data, presenting it to the user, and providing quantifiable metrics to facilitate decision making.

The systems and methods described herein may be used in the field of neurosurgery, including oncology care, neurodegenerative diseases, stroke, brain trauma, and orthopedics; however, the skilled artisan will appreciate that these concepts can be extrapolated to other conditions or areas of medicine.

Various devices or processes are described below to provide examples of embodiments of the planning and navigation methods and systems disclosed herein. The embodiments described below in no way limit any claimed embodiments, and any claimed embodiment may encompass processes or devices different from those described below. The claimed embodiments are not limited to devices or processes having all the features of any device or process described below or to features that are common to multiple or all of the devices or processes described below. The devices or processes described below may not be embodiments of any claimed invention.

In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, one of ordinary skill in the art will appreciate that the embodiments described herein can be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the embodiments described herein.

Additionally, this description should not be seen as limiting the scope of the embodiments described herein.Furthermore, in the following passages, different aspects of the embodiments are defined in more detail.

Provided in the present disclosure is a software and hardware system for providing diagnostic, surgical planning, surgical guidance, and follow-up imaging information to support surgical procedures and image-guided treatment procedures. In one embodiment, an exemplary system is comprised of a computer processing unit, software algorithms, a display unit, input/output devices, imaging modalities, and device tracking devices to facilitate the presentation of medical imaging information used to facilitate surgical procedures. The system focuses on minimally invasive surgical approaches to manage neurosurgical conditions and head and neck cancers; however, it is not limited to these applications. These concepts can be used to address conditions throughout the body where minimally invasive approaches can be used in conjunction with preoperative imaging and/or intraoperative imaging. The system is described in the context of neurosurgical applications; however, the general concepts can be extended to various applications further described herein.

The present disclosure describes methods and systems for preoperative, intraoperative, and postoperative planning and navigation to achieve minimally invasive surgical procedures. The systems and methods can be used as surgical planning systems and methods, or as combined planning and intraoperative guidance and navigation systems and methods, wherein information collected during a surgical procedure is used to guide subsequent surgical steps or to measure expected patient outcomes.

In one embodiment of the methods and systems of the present invention, one or more sensors are provided that detect inputs such as preoperative data inputs and intraoperative data inputs, and the sensors are connected to one or more processors that receive, record and/or process the inputs detected by the sensors to generate outputs that may be useful for surgical planning, navigation and analysis.

FIG1 illustrates an embodiment of the method and system of the present invention for use as a multimodal surgical planning tool. The system and method can be used as a surgical planning tool in the preoperative phase. The skilled artisan will appreciate that the surgical planning steps depicted in FIG1 can also be repeated intraoperatively to further refine the surgical approach, such that the terms surgical planning and intraoperative navigation can be used interchangeably.

In some embodiments, the systems and methods may include data inputs including, but not limited to, MRI (6), US, CT, other optical imaging systems, and models of surgical tools (1) and sensors. Imaging data may be obtained by comparing various images of the patient's tissues and organs, including data co-registered between DWI (diffusion weighted imaging) (4), DTI (diffusion tensor imaging) (3), and other imaging contrast sequences and modalities. In one embodiment in which the present invention is used to set or update a surgical path in an intraoperative context, the data inputs may include instances of the above-mentioned imaging acquired by the sensors, as further disclosed herein. The sensors may include means for accurately and robustly tracking the surgical tools (including optical or electromagnetic intraoperative tracking components) and other means for registering (15) the intraoperative dataset with the preoperative dataset. Registration methods may include, for example, any one or a combination of the following: image intensity matching based on a similarity measure (such as the sum of squared intensity differences) and mutual information calculated over some neighbors or regions; image feature-based registration, for example, edge matching; fiducial or anatomical feature-based matching of common points defined in multiple image modalities or coordinate spaces (for example, the coordinate space of the tracking system and the coordinate space of the MR image); surface matching techniques, for example, surface mesh matching.

The surface can be manually outlined or automatically segmented from the image data. Similarly, the surface can be determined from the actual patient by outlining with a tracked pointer tool or by surface scanning techniques (e.g., laser rangefinders, structured light systems, or stereo cameras). All matching and registration methods can be performed on sub-regions of the image or patient volume (e.g., objects visualized through ports) to focus on specific areas of interest. Registration can be performed on multiple sub-regions collectively or independently, and interpolated registrations between these independent regions can be inferred. Once the images are registered, they form the input to the data analysis module (16).

The skilled person will appreciate that the sensors may also include planning, navigation and modeling components, contextual interfaces, intraoperative imaging devices, devices for bipolar suction, tissue ablation and tissue cutting utilizing attached imaging, tracking technology, including external and internal tool tracking (optical deflection, capacitive, strain gauges), automatically guided external imaging systems, semi-automatic external positioning arms with turntables, internal semi-automatic manipulators, multi-beam delivery systems, databases with adaptive learning networks, imaging and spatially linked pathology systems, imaging devices corresponding to the context in which they are used, and user interfaces corresponding to the context and environment in which they are used.

Inputs and sensors may also include a keyboard, touch screen, pointer or tool used as a pointing device, mouse or gesture control component.

Preoperative data inputs for the exemplary systems and methods described herein may include preoperative image data, biomechanical models of tissues and organs, and mechanical models of surgical tools. Intraoperative data inputs may include images from various modalities including MRI, CT, or PET, as well as data from tracking or navigation systems, including tracked surgical devices such as scissors, ablation devices, suction cutters, bipolar devices, tracked access port devices, and automatically guided external imaging systems. In some embodiments, for example, a specific surgical procedure 14 and clinical criteria 13 selected for each patient may be used as additional input to evaluate the optimal surgical plan.

In some embodiments, the processor may include a planning module 12 that analyzes inputs from 13 and 16 to define surgical approaches. These approaches may include open craniotomy, DBS stimulator location, biopsy site, port-based or minimal access approaches, and endonasal-based approaches based on various inputs and rule-based calculations. In further embodiments, the processor may include a navigation module that analyzes inputs to provide visualization and other outputs during the procedure, such as tool tracking and contextual information.

In other embodiments, the processor may segment tissue structures such as tumors, nerves and nerve bundles, brain structures such as ventricles, sulci, cortex, white matter, major white matter tracts, blood vessels such as arteries and veins, and bony structures such as the skull and brainstem for planning and navigation purposes.

Outputs may include 2D and 3D composite images for guidance, including tissue extraction guidance and guidance for devices including DBS probes and biopsy probes. A skilled artisan will appreciate that output devices including monitors or laser pointers may also be included in the systems and methods described herein to provide feedback to the user regarding the system's progress.

Visualization outputs may include: contextual volumetric imaging; point source imaging, which involves imaging of areas of interest that are important only at that moment in the surgical procedure; imaging for checking positioning before instrument insertion or removal; imaging for updating tissue maps after resection; and imaging for maximizing tumor resection while limiting damage to healthy or reparable tissue. Furthermore, the use of a common contrast mechanism between imaging modalities used in the systems and methods described herein may allow the processor to generate accurate registration between modalities and meaningful volumetric imaging updates during the procedure.

The output may also include path planning or correction data for the surgical approach with the aid of feature detection, positioning for procedures such as craniotomy, locking and fixed positions for patients. The output may also include data on the selection of the surgical approach (e.g., a transsulcal approach to avoid blood vessels and fiber bundles). For example, the output may also include a sulcal-based entry path that minimizes white matter and gray matter insertion damage. In addition, the output may include parametric curves or volumes to define or facilitate the temporal evolution of data such as the following: a selected path, tissue deformation, real-time animation of a data set with a temporal component (e.g., Doppler ultrasound or fMRI), or any combination of such data.

General planning methods for any part of the patient's body

Disclosed herein is a computer-implemented planning method for planning a surgical trajectory path from a surface location on a patient's body to a target location within the body to be approached and operated on. The planning method is highly general and can be applied to any part of a patient's body. The method comprises: acquiring preoperative images of the portion of the patient's body to be operated on using at least one imaging modality configured to acquire a 3D image dataset or volume; and storing the 3D image dataset or volume in a storage medium. It should be understood that more than one imaging modality may be used, particularly where the anatomical portion of the patient to be operated on is best suited for a particular type or combination of imaging modalities. A 3D volumetric image is generated from the 3D image dataset, the dataset containing potential entry points into the body and one or more targets to be approached. The 3D volumetric image is stored in the storage medium. Once the locations of the one or more targets are identified, the locations of these targets can be adjusted and/or confirmed on 2D planar estimates or projections of the data, a technique known as "reformatting." This technique visualizes representations of one or more 2D planes through the 3D space containing the image data. Such planes are often orthogonal and are often displayed in standard (axial, coronal, sagittal) orientations as "multi-planar reconstructions" or "MPRs." There are other variations, such as a "radial stack" where one or more planes are displayed with a common axis about which they are all rotated. However, it should be understood that any configuration of planes may be used, containing image data from a single source or a fusion of multiple sources. In the presence of 3D data (e.g., from MRI, CT, or 3D ultrasound volumes), the reformatted images may be generated by interpolating from the sampled lattice using any suitable standard interpolation scheme. If the desired data is two-dimensional in nature (e.g., X-ray or 2D ultrasound), the data may be projected onto the reformatted planes, either by providing only their plane intersections or by merging the two methods as desired. Once the reformatted planes are provided to the user, the user may adjust each plane position within the 3D space and refine the positioning of the objects relative to each plane representation until they are satisfied such that they have identified the correct position in the 3D space.

Using a 3D volumetric image, the method includes: designating the location of at least one entry point for a surgical device into the patient's body; and specifying a specific target location to be approached from the one or more target locations. The location of the one or more potential entry points and the target location can be designated in one of a variety of ways. For example, a clinician can select an entry point by superimposing a mouse cursor on a point on the 3D rendered brain surface and clicking. Alternatively, the system can be programmed to automatically select or suggest potential entry points based on a certain criterion (e.g., using a sulcal path for entry). For example, given an image volume (e.g., a T1 MRI image), a segmentation of the image (into white matter, gray matter, dura mater, and sulci) including labeling of various image components, and a target, the system can be used to restrict or suggest certain entry locations. The system can generate optimal sulcal entry points based on, for example, minimizing the number of affected fibers, the distance from the sulcal boundary to the target, and the volume of white matter and/or gray matter displaced by the entry path. Such points can be found through an exhaustive search or various standard methods (e.g., energy minimization). The simple approach can be improved by utilizing additional information (more segmentation labels, biomechanical modeling, fluid dynamics modeling) to apply more advanced analysis to generate "best" candidate points. The surgeon will choose from these "best" candidate points, or can reject them and manually select one.

One or more surgical intents or surgical outcome criteria are then selected to be met by the surgical trajectory path from the entry point to the specified target location, and optionally one or more waypoints between the designated location of the entry point and the specified target location consistent with the surgical intent may be selected based on the surgical intent.

In another embodiment, a surgical path can be tracked and recorded using a navigation system while a clinician using a tracked tool attempts different approaches to a target in a brain model (which is fabricated to simulate actual patient anatomy). The one or more point-by-point surgical trajectory paths are then used to calculate one or more point-by-point surgical trajectory paths from the specified entry point to the specified target location, passing through one or more waypoints between the entry point and the specified target location, to define a surgical trajectory path from the specified entry point to the selected target location. These trajectories can be manually specified by the clinician or automatically calculated. Exemplary automatic calculations may include the following. Given an MRI T1 image and a surgical entry point and a target specified within the image, the system specifies a lattice (e.g., image voxel centers or any other lattice chosen for convenience). The lattice infers a graph of connections between all adjacent voxels for a selected connectivity scheme (i.e., it may allow only neighbors in 6 directions (without diagonal connections) or full connectivity in 27 directions, or any other subset). Each connection is assigned a weight (i.e., a cost) based on the integrated pixel intensity along the direct path between the lattice points. We now apply a standard path-finding algorithm (e.g., the A* search algorithm, e.g., Hart, P.E.; Nilsson, N.J.; Raphael, B. (1968). "A Formal Basis for the Heuristic Determination of Minimum Cost Paths". IEEE Transactions on Systems Science and Cybernetics SSC4 4(2):100–107) to determine the optimal path. Variants of this method may include more terms in the cost function based on labeled regions of the brain, biophysical modeling, fluid dynamics, etc., if available. Variants may also include post-processing of the path (e.g., smoothing) as needed. Waypoints may also be added by the clinician after the automatic calculation of the surgical trajectory path.

Once the one or more point-by-point surgical trajectory paths have been generated, they can be stored in a storage medium and visually displayed to the clinician. The surgeon verifying the intent can select the one or more surgical intents from a list of surgical outcome criteria displayed on a computer display screen by overlaying the mouse over the one or more listed intents and clicking to close them. Additional embodiments may include the use of a touch screen or stylus and a monitor connected to a video tracking system or other device that delivers gesture input or voice input. These surgical outcome criteria will be different for different parts of the anatomy being treated, for example, the list of criteria may be different for brain surgery than for spinal surgery.

The step of selecting the surgical intent or surgical outcome criteria to be met by the surgical trajectory path may include: selecting one or more anatomical features to be avoided (or to have minimal damage caused to them); or alternatively, selecting one or more areas to be traversed by the surgical path, which can also be performed by the surgeon by placing a cursor on a specific location to be avoided or traversed and clicking the cursor to store such a specific location. Once the selection is made, the location of the one or more anatomical features is identified from the 3D volume image, and one or more surgical paths that avoid or traverse the one or more anatomical features can be calculated as needed. Typical non-limiting examples of anatomical features to be avoided or to have minimal damage caused to them include any one or a combination of nerve damage, muscle damage, ligament damage, tendon damage, vascular damage, white matter tract damage (in the case of brain surgery).

The identification of such structures can be provided to the system by defining the region of interest, labeling mapping or other metadata relative to the imaging volume. Alternatively, the system can automatically estimate such structures and use them in other analyses. One method of doing so would be to co-register a detailed brain atlas with the image volume being used, and then use the atlas labels used as input for the above. Such co-registration can be achieved by constructing an atlas relative to a template clinical image (a representative sample or possibly an averaged image) and then performing co-registration of the template. An example is shown in "Medical Image Registration", Derek L G Hill et al 2001 Phys Med. Biol. 46R1. This information can be used as further input and constraint to automated trajectory calculation algorithms such as those described hereinbefore.

Table 1 summarizes this variation for various types of surgical procedures. While it is clearly highly desirable to avoid many anatomical features, there may be situations where the surgeon actually does wish to access and traverse an anatomical feature. Examples of this include deep brain stimulation, resection of multiple tumors, and transsulcal approaches.

The least affected structure

Table 1

The method also includes assigning scores to the one or more trajectory paths to quantify the extent to which the one or more trajectory paths conform to the surgical intent, and calculating an optimal surgical path based on a comparison of these scores. Some non-limiting examples of metrics that are relevant to the surgical intent of general surgical procedures on any anatomical body part and that are considered when calculating scores associated with alternative surgical trajectories are listed below:

1. For surgical procedures involving structures such as, but not limited to, nerves, blood vessels, ligaments, tendons, organs, etc., the angle of incidence of the surgical path relative to each structure can be used to determine the average amount of damage expected to be inflicted by the structure, wherein steeper angles of incidence (closer to being orthogonal to the structure) will cause more damage and therefore correspond to worse scores than more parallel angles of incidence (closer to being parallel to the structure), and more parallel angles of incidence will cause less damage and therefore correspond to better scores. In addition, the number of structures expected to be critically intersected can be used as an extension of the metrics described herein.

2. The length of the surgical path can also be used to score the trajectory. For example, depending on the type of surgical device, its shape, and size, a longer trajectory may cause the device to apply force over a larger area, potentially resulting in greater overall trauma than a shorter path. Therefore, in this case, a shorter path would correspond to a better score, while a longer path would correspond to a worse score.

3. The number of path points used for specific changes in direction can also be used for scoring. For example, if the surgical device is rigid, the higher the number of direction changes that occur and the larger the angle of the direction changes, the more the tissue is forced to deform. This deformation of the tissue relative to the surgical device in various orientations can cause additional internal strain and wear on surrounding tissue, causing damage to it. Thus, a higher number of direction changes and higher angles of direction changes will correspond to a lower surgical path score. In the case of tumor resection, the angle of incidence at which the surgical path intersects the tumor boundary can also be used for scoring. Because a substantially tangential path is more likely to cause the surgical device to miss the tumor, slip off the tumor without properly incising it, or cause the tumor to roll relative to surrounding tissue, thereby causing greater stress on surrounding healthy tissue, such a path should be assigned a poorer score. In contrast, a surgical path that intersects the tumor at an orthogonal angle of incidence will be assigned a better score.

4. In other examples, the organs or structures penetrated by the surgical path can also be considered in the path scoring. For example, in spinal surgery, ideally, specific ligaments should not be penetrated because they are critical for the effective function of the joint - the less damage to these ligaments, the better the corresponding score for that particular path.

5. The surgical path score can also be weighted based on patient recovery statistics derived from previous surgeries within the same setting. For example, after a similar path has been used (X) times for a particular surgery, the patient recovery rate is (Z1), compared to an alternative path used (Y) times with a patient recovery rate of (Z2). In one exemplary embodiment, the "similar path" metric will identify paths similar to the proposed surgical path to be scored based solely on the anatomical location of the target within the standard atlas (i.e., where the surgeon would describe the location of the target) and the corresponding location of the entry point based on the same atlas. More details can be added based on additional resolution or pathology (e.g., type of tumor) or based on detailed statistics or metadata of the surgical path followed (e.g., interaction with anatomical features from the atlas). Other criteria that can be used in evaluating "similar paths" would be the type of tumor resected from a given path, the specific ligaments penetrated from a given path, the age of the patient, the location of the tumor, the organ/region of interest penetrated, etc. Thus, a shorter recovery time (Z) would correspond to a better score for that particular surgical path.

6. The proximity of blood vessels to a particular path can also be used to score the surgical path, as the fewer blood vessels (veins and/or arteries) affected, the less trauma the patient experiences. Therefore, the lower the number of blood vessels near the path, the better the score.

7. The length of tissue penetrated can also be used to score the surgical path, as penetrating tissue is generally much more traumatic than simply pushing it aside. In this case, a path that requires more tissue cutting will be given a worse score than a path that requires less cutting. In addition, the type of tissue cut or penetrated will also affect the score.

8. Another metric would be the fragility of the tissue being traversed, as highly fragile tissue is generally more likely to sustain damage under manipulation than more resilient tissue structures. In this embodiment, a map and/or database can be used to derive the most likely value for the specific area traversed by the surgical path under consideration, or this information can be derived from direct tissue density or elasticity measurements, such as from ultrasound or MR elastography. In yet another embodiment, tissue fragility can be inferred from known properties of the tissue, including but not limited to the hardness or stiffness of the tissue.

These metrics will vary depending on the surgical tools being inserted and the surgery being performed. Therefore, the scores assigned to the alternative trajectories will incorporate both the type of surgery and the specific tools planned to be used in the procedure. This also provides the surgeon with an opportunity to evaluate the pros and cons of using different surgical techniques and tools for a particular procedure.

The method may also include comparing the scores of the one or more point-by-point surgical trajectory paths to the surgical intent score of a path that provides the shortest distance between the one or more target locations and the nearest entry point. It should be noted that the majority of currently performed surgeries currently utilize a straight path from the surface to the target, corresponding to the shortest distance. Therefore, the method compares the scores between the more prominently used shortest distance surgical trajectory path and the suggested alternative path, thereby alerting the user to the difference for future consideration. In some cases, a straight path approach may provide the best score, in which case the user may also consider the straight path approach.

The clinician (typically a surgeon) designation of the one or more potential entry point locations and target locations, the first target to be approached, and surgical outcome criteria are inputs that can be communicated by the clinician to the computer and stored in the computer storage device.

It should be noted that the methods and systems of the present invention can be configured to be highly automated, requiring minimal input from a clinician. For example, a computer processor can be programmed to determine the location of one or more surgical targets to be approached by comparing an image of a 3D volume of a patient's anatomical structure with an anatomical atlas and/or a library of stored images of normal, healthy tissue, as described herein. The computer processor can be programmed to select one or more potential entry points, then calculate one or more surgical corridors to the first target to be approached, and then score each corridor based on a stored set of surgical outcome criteria associated with the specific anatomical portion being operated on. The computer then compares the scores and selects the corridor with the best score for the specific set of surgical outcome criteria.

Once the one or more surgical paths are determined, the surgical/clinician team may wish to run a simulation such that the system is programmed to visually display a simulation of the surgical tools approaching the target along the one or more surgical paths and touching all parts of the target to be engaged by the surgical instruments.

Exemplary Brain Surgery Planning Method

FIG5 illustrates the processing steps involved in the planning system using a flow chart. The first step involves acquiring preoperative images of the patient (as shown in step 500 in FIG5 ). The image data set is first imported into the software from a database or server such as a PACS server. The preoperative surgical planning method and system uses preoperative images (i.e., those images obtained before the surgical procedure is started) obtained using at least one or any combination of MRI, CT, PET, or similar modalities that have the necessary tissue penetration to image the desired portion of the brain before the invasive procedure begins, and these images typically include fiducials or other markers for orienting the imaging in space.

The planning method and system of the present invention can also advantageously use more than one imaging mode. In this case, images from different modes are co-registered with each other to obtain combined information. For example, in one embodiment, an MRI can be obtained under conditions suitable for both acquiring diffusion (usually DTI) data and obtaining MR data that can be used to generate a 3D sulcal surface map. These preoperative MR images used to obtain the diffusion images are co-registered with each other, just as they are also co-registered with the MR images used to obtain the 3D sulcal surface map (as shown in step 510 in FIG5 ), because each MR imaging mode will have its own orientation, geometric scale, and deformation.

As discussed herein, the co-registration process (510) is a generally known process in which appropriate transformations are applied to the images so that they match each other geometrically and thus anatomical regions overlap in images obtained using various modalities. A commonly used algorithm for co-registering images is "PET-CT image registration in the chest using free-form deformations" IEEE Transaction on Medical Imaging, Vol: 22, Issue: 1, (2003). Once the DTI and 3D sulcus surface map are generated, the method involves superimposing the DTI data onto the 3D sulcus map data. The 3D sulcus map is constructed using MR data to generate a 3D surface map, which is then used to represent the brain surface and clearly show the sulcus folds or cracks present on the brain. After removing the skull structure from the acquired image, the 3D sulcus map is constructed from the T1MR image. An exemplary algorithm for removing the skull (also known as skull stripping) is given in "Geodesic Active Contours," Vincent C. et.al., International Journal of Computer Vision 22(1), 61-79 (1997). This superposition of the sulcal map and DTI helps detect co-registration errors because an erroneous DTI estimate will appear as a brain fiber bundle protruding beyond the sulcal border or into the gyrus. Such deviations can be quantified to obtain a score or measure of the quality of the co-registration between the various imaging modalities. An exemplary algorithm for quantifying the registration error at this stage is as follows: the ratio of the length of the bundle contained in the white matter boundary to the total length of the bundle. Ideally, this metric should be as low as possible. 1 minus this ratio can be used as a goodness of fit measure for evaluating the quality of the DTI estimate relative to the available brain map.

The scoring process provides a measure of the "goodness of fit" between the 3D sulcus map and the DTI data. If the score between the 3D sulcus map and the DTI data indicates that there is an unacceptable amount of registration deviation, remedial measures will be needed to improve the score before completing the planning procedure. The remedial measures may include re-estimating the fiber bundle imaging data (DTI) using a different starting region, which is selected by the user or automatically selected nearby but does not overlap with the initial seed point. The starting region or point set is typically used in DTI estimation to estimate the voxels that collectively represent each bundle.

A common source of error in DTI estimation is the selection of the wrong principal direction for the fiber bundle passing through a given voxel. The above-described selection of different starting points for bundle estimation can force the selection of alternative principal directions for a given voxel and, therefore, avoid estimating bundles that extend into sulci or beyond the brain surface. It should be understood that DTI estimation is an optimization process and that one of many commonly available estimation methods can be tried to obtain alternative bundles, and that an anatomically reasonable set of bundles can be retained for subsequent processing. The anatomical correctness of a DTI estimate can be judged by a human reviewer or automatically by a software algorithm that estimates the same goodness-of-fit measure described above while utilizing additional information, such as an anatomical map of the brain showing the relative concentrations of bundles in known regions of the brain.

This approach can be complicated by the fact that the presence of a large tumor may geometrically distort the beam around the tumor region. An innovative aspect of the system of the present invention is that it can minimize the effect of this distortion on the goodness of fit measure by limiting its estimate to the side of the brain least affected by the tumor. Tumors requiring surgical intervention are often confined to one side of the brain. This information is known a priori because the diagnosis of the tumor is completed before surgical planning begins.

After reviewing the results of the process using visual confirmation and evaluation of the co-registration scores, and taking the steps discussed above to obtain overlapping data that is substantially free of unacceptable deviations if deviations are found, specific regions of interest can be defined on one or more images, as shown in step (520). Using a computer interface, the clinician can define regions on one or more 2D image layers, and the corresponding volumes of interest can be defined by interpolating between such defined regions. Alternatively, a specific point can be selected by the user to obtain an initial estimate of the region of interest (ROI), and a software algorithm can be used to identify the region in the 2D image layers. A common method for identifying such regions is called connected component labeling. This is described in detail in the following references and in Computer Vision, D. Ballard and C. Brown, and is commonly used in graphics processing.

Alternatively, such ROI can be established by image segmentation. Such ROI can be generated manually or automatically for multiple 2D layers, and the volume of interest (VOI) can be established by interpolating between such ROIs in 3D space. Equally, common techniques such as spline fitting can be adopted here. VOI can be visualized relative to an anatomical atlas, which can be superimposed on MR, CT or sulcus maps drawn in 3D. ROI and/or VOI can serve as landmarks of lesions that need to be processed or key areas that must be avoided during surgical procedures. In the scenario where ROI or VOI represent lesions or regions to be removed, surgeons use them as target regions. Volume also provides an estimation of the quality of lesions, tumors or other regions that must be removed, which may be useful for clinicians during surgery. In addition, intraoperative imaging can be used to assess the reduction of the volume of the target region during the entire surgical procedure. In an alternative scenario where the ROIs or VOIs represent areas to be avoided, the surgeon uses them as landmarks where he/she must maneuver carefully to preserve these areas while still being able to reach the diseased area (e.g., lesion, tumor, blood clot, etc.).

Areas to be avoided that are consistent with the desired surgical outcome can be defined as "no-fly zones" for the surgeon to avoid, thereby preventing potential damage to the patient's motor, sensory, or any other critical functions. Thus, the ROI and/or VOI can be specifically defined for a patient based on the specific functions that are desired to be preserved for that patient. Thus, the ROI and VOI help define a surgical path that is uniquely tailored to the specific functions that need to be preserved for the patient.

Some non-limiting examples of metrics that are relevant to surgical intent specific to brain surgery and that will be considered when calculating scores associated with alternative surgical trajectories are listed below:

1. For brain surgery, the angle of incidence of the surgical path relative to each fiber tract can be used to determine the average amount of damage expected to be inflicted by that tract, where steeper angles of incidence (closer to being orthogonal to the tract) result in greater damage and therefore correspond to a worse score than parallel angles of incidence (closer to being parallel to the tract), which result in less damage and therefore correspond to a better score. A basic implementation would take the absolute value of the cosine of the angle between the surgical path and the orientation of the intersecting fiber tracts. For example, we could assign a score of 1 to parallel tracts and a score of 0 to perpendicular tracts, and set a threshold score below which a neural fiber tract is likely to be severely transected. This number of severely transected tracts can be used as an extension of the described metric, in that, for example, the score of a path can be divided by or subtracted from a function related to the number of severely transected tracts, thereby reducing the score of the path.

2. For brain surgery, tracts that are severely transected by the surgical trajectory can be tracked to identify the brain regions connected by these tracts. Using this information and, for example, a brain atlas, the function of the neural tracts can be determined (i.e., assumed) and used to score the pathways accordingly. In this case, the functions that are best preserved for a particular patient (determined by the surgeon and patient) will be prioritized and assigned a worse score than other neural functions when transected. For example, for a professional guitar player, preserving motor function in the upper limbs may be prioritized over other functions. Such regional analysis can be performed by a clinician and provided to the system as a series of regions of interest or labeled images.

3. The length of the surgical path can also be used to score the trajectory. For example, in the case of port-based brain surgery, a longer trajectory can cause the device to exert force over a larger area, potentially causing greater trauma to the brain than a shorter path. Therefore, in this case, a shorter path would correspond to a better score, while a longer path would correspond to a worse score.

4. The number of path points used for specific changes in direction can also be used for scoring. For example, in port-based brain surgery, given that the port is rigid, the higher the number of direction changes that occur in the path, and the greater the angle of the direction change, the more the brain tissue will be forced to deform. This deformation of the tissue at various orientations relative to the port can cause additional strain and wear on the affected surrounding tissue (particularly, nearby nerves and nerve bundles). Thus, a higher number of direction changes and higher angles of direction changes in the ports along the surgical path will correspond to a lower surgical path score.

5. In the case of tumor resection in the context of port-based brain surgery, the angle of incidence at which the surgical path intersects the tumor boundary can also be used for scoring. Since a substantially tangential path will be more likely to cause the port to miss the tumor, fail to engage (slip off) the tumor, or cause the tumor to roll relative to healthy tissue, all of which require more movement of the port and therefore greater stress on the surrounding healthy brain tissue, this path should correspond to a poorer score. However, in contrast, if the surgical path is at an orthogonal angle of incidence when intersecting the tumor boundary, this path will result in a better score.

6. The surgical path score can also be weighted based on patient recovery statistics derived from previous surgeries in the same setting. For example, after a similar path has been used (X) times for a specific surgery, the patient recovery rate is (Z1), compared to an alternative path that has been used (Y) times with a patient recovery rate of (Z2). In one exemplary embodiment, the "similar path" metric will score paths based solely on the anatomical location of both the path's entry point and target point (as described by the surgeon relative to a standard atlas). Paths that share or are near locations according to the atlas will therefore be considered similar. More details can be added to make this metric more discriminatory. For example, additional resolution can be added to the location (e.g., specific location within each sulcus used for entry) or pathology (e.g., tumor type) or detailed statistics or metadata of the surgical path followed (e.g., percentage of white or gray matter displaced or interaction with anatomical features). Other criteria that can be used in evaluating "similar paths" would be the type of tumor removed from a given path, known mechanical properties of brain tissue in the affected area, age of the patient, location of the tumor, affected area, etc. Thus, in this example, a shorter recovery time (Z) would correspond to a better score for that particular surgical approach.

7. The proximity of blood vessels to a specific pathway can also be used to score the surgical pathway, as less damage to these vessels will obviously reduce the trauma suffered by the patient. Therefore, the lower the number of blood vessels near the pathway, the better the score.

8. In the case of port-based brain surgery, the amount and type of tissue penetrated can also be used to score the surgical path, because penetrating brain matter is much more traumatic than simply pushing it aside. In this case, penetrating more tissue will receive a worse score than penetrating less tissue. In addition, the type of tissue penetrated will also affect the score. For example, penetrating white matter will receive a worse score than gray matter, given its neurological importance, because damage to gray matter is generally less important (for most cases) to the overall health hazard posed to the patient by penetrating brain matter. Therefore, when two different paths are used to penetrate the same amount of gray matter and white matter, the path that penetrates the white matter will receive a worse score than the path that penetrates the gray matter.

9. Another metric would be the stiffness of the tissue being traversed, as more rigid tissues are more likely to sustain damage under manipulation than more flexible tissue structures. This would require the use of atlases and/or databases to derive the most likely value for the specific area traversed by the surgical path under consideration.

10. Another metric would be to include brain function as part of the pathway score. Brain function can be measured using functional MRI (fMRI) information (BOLD contrast imaging), magnetoencephalography (MEG), Raman spectroscopy, or electrophysiological measurements. Pathways that pass through regions with high levels of function, high rankings in the brain functional hierarchy (regional importance), or that are functionally related to such regions will have a poorer score. In addition, pathways that pass through white matter tracts connecting such functionally related regions will also have a poorer score.

Once the region of interest has been defined, one or more targets can be identified in the image (as shown in step 530). The targets correspond to three-dimensional locations within the brain that must be reached to remove the tumor (or lesion). It is known that in order to spatially accurately locate a point in 3D space, a minimum of three orthogonal planes are required. However, additional views can be provided, wherein these additional views contain images obtained using different modes. In other words, the additional planes can geometrically overlap with the above-mentioned orthogonal planes and provide images captured using other modes that are complementary to the modes presented in the above-mentioned three orthogonal planes. For example, the three orthogonal planes can represent T1MR image slices, while the additional views can provide co-registered images obtained using CT or B0 (another MR data representation). Complementary modes help confirm the location and extent of a tumor or blood clot. Another redundant means of providing information that helps estimate the location of a tumor is to provide data as radial slices, wherein virtual slices are generated so that the slices are along planes positioned radially around a user-defined axis.

Visualizing the target being operated on in multiple 2D images reduces the inherent risk of placing the target in 3D space using only 3D rendering of the brain (see FIG7 ). The latter approach is prone to error because the 3D surface is drawn on a 2D display. It should be noted that a 3D holographic display can also be used to overcome this risk because the surgeon will be able to view the 3D virtual object from multiple perspectives to confirm the position of the target. In one embodiment, this can be used as an alternative to providing image data in three orthogonal planes.

Another inventive aspect of the present invention is the ability to visualize white matter tracts immediately adjacent to a target. This functionality is achieved by hiding diffusion tracts (or fiber tractography information) in all regions of the brain, except for tracts intersecting the geometric space occupied by the target region or within the immediate vicinity (within a threshold). Alternatively, tracts intersecting the geometric space occupied by surgical tools actually inserted into the brain can be displayed. Such tools can be virtual representations of biopsy needles, ports for minimally invasive surgery (e.g., access ports), deep brain stimulation needles or catheters, and the like. This method of selectively displaying DTI information helps manage the large data challenges associated with visualizing the entire DTI image. It also helps surgeons narrow their focus and primarily visualize the affected tracts, rather than all fiber tractography information associated with the entire brain. This selective filtering of the depiction of white matter tracts immediately adjacent to the target region or predicted to be affected by the tool allows the surgeon to view tract information within a selectable translucency, facilitating selection of a surgical path that best meets the surgical intent. Furthermore, this optional display of DTI information can similarly be replaced or supplemented with any other co-registerable modality, including fMRI or other modalities capable of assessing brain function that may potentially be affected during tumor resection.See Figures 8 and 9 for illustrations of tract intersection visualization.

The system can be programmed to provide a histogram analysis in which a histogram of the number of fibers to be displayed versus a threshold shear cut-off angle is calculated. This provides information about sensitivity to that threshold. In one embodiment, if there are values where the number of fibers to be displayed jumps (i.e., for a small change in a given cut-off threshold, there will be a large change in the display), the software can suggest alternative cut-off angles around the set cut-off.

Alternatively, instead of a binary cutoff threshold, the display may be modulated to provide a gradual change in the displayed fibers (eg, by reducing fiber intensity or increasing transparency) as the intersection angle increases beyond a set threshold, or between a minimum set threshold and a maximum set threshold.

Another embodiment may involve distance analysis, wherein the system and method are configured to display only a set distance from each bundle to its intersection with a port, rather than the entire path of the bundle, because fibers farther from the intersection are less likely to be affected. This distance threshold can be dynamically adjusted and manipulated. The display of each bundle can also be modulated by the distance from the port intersection (e.g., by reducing brightness, changing color, increasing transparency, or reducing the thickness of the displayed bundle with distance).

Alternatively, the displayed beams may be similarly modulated by the distance from the intersection with the port to the endpoint, since a beam affected at or near the endpoint of the beam may be less affected than a beam affected further along its trajectory.

The next step in establishing the surgical path is to identify the entry point, also referred to as the junction point (as shown in step 540). It should be noted that this entry point refers to the point of entry of the leading portion of the dura mater of the brain by the surgical port tool. There may be another entry point for the surgical port to enter the white matter of the brain. The above-mentioned first entry point is established by visualizing the sulci by superimposing a virtual entry tool such as a port tool, biopsy needle, catheter, etc. However, an advantage of the present invention is that the virtual port tool can be presented in such an approach in an unobstructed manner by representing it as a semi-transparent model of the tool.

Then, the target and junction can be used as navigation references to define the sulcus path (as shown in step 550). In one embodiment, the method and system of the present invention are configured to define a segmented linear sulcus path, which includes a junction and a target point as the starting point and end point of the two extremes in the surgical path, as well as additional spatial locations between the two extremes. When a bend is observed in the sulcus, these additional spatial location points can be inserted to define the segmented linear path. A segmented linear path that closely follows the bend in the sulcus can best preserve the brain area contacted by the surgical tool, wherein such a surgical tool is thin and/or flexible or articulated. Therefore, it is foreseeable that an articulated or flexible port utilizes such a segmented linear path to further reduce trauma to the brain. A metric or score can be associated with a specific sulcus path to indicate the range of brain tracts that are transected by the virtual port. Therefore, the score can be used as a measure of the trauma that is expected to be introduced by the port when using the planned sulcus path. In other words, the number of tracts that are transected can be used to compare two or more different paths to identify the path that presents the least number of tract intersections.

Finally, by modeling the surgical tools and evaluating the range of motion available for each tool when the movement of the tool is subject to the size and position constraints of the craniotomy, the alternative positions and geometries for craniotomy (as shown in step 560) can be evaluated. This range of motion can be seen in Figure 10. In addition, the craniotomy position and sulcus path can be more accurately visualized by radially stacking image slices. In other words, the 3D reconstructed MR images of the entire brain can be used to constitute virtual 2D image slices sharing a common axis that is reasonably close to the planned sulcus path. Such slices expose the extent to which the sulci approach the planned path and therefore contribute to better visualization of the alternative sulcus path. A final scorecard is formed to provide all metrics from each of the previous stages and a metric for representing the fitness of each of the defined sulcus paths. The fitness of the sulcus path (also referred to as sulcus consistency percentage) is the ratio of the total length of the planned sulcus path to the described sulcus path and the sum of the Euclidean distances from the path end to the target. Then, this ratio is multiplied by 100 to express the ratio as a percentage. This metric indicates the consistency between the linear trajectory and the selected sulcus path. 100% indicates a perfect match or a linear path.

The created surgical plan is then stored and/or exported to a navigation system (570), which typically receives such data and stores and/or co-registers (if necessary) such a plan or surgical path for use by the surgeon in navigating his or her surgical tools during the surgical procedure. An inventive feature of the planning system allows the surgeon to visualize the entire procedure and compare alternative surgical plans by automatically replaying the surgical steps as a video. This helps the surgeon visualize the entire procedure and thus serves as a confirmation step and a training step for the surgeon.

If the medical procedure is to address an urgent medical emergency and there is no time to obtain images from multiple imaging modes, the methods and systems of the present invention can be configured to use a single non-invasive imaging mode. In this case, a planning method for planning a passage from the sulci to the location in the patient's brain where surgery is to be performed includes acquiring preoperative images of the patient's brain where surgery is to be performed using a non-invasive imaging mode and co-registering the preoperative images. The co-registered images are used to identify the sulcal structures of the patient's brain and one or more targets and associated one or more target locations to be approached and operated on during the invasive surgical procedure. The one or more target locations can be visually displayed in at least three orthogonal planes to confirm the location of the one or more targets in 3D space. Based on the location of the entry point and the one or more target locations, a segmented linear surgical path is defined, wherein the location of the entry point and the location of a selected one of the one or more target locations are designated as the starting point and end point in the surgical path, respectively. The surgical path is selected to avoid passing through selected anatomical features of the brain.

After the planning phase is completed and the surgery begins, once the brain tissue is visualized, other imaging modalities other than the aforementioned MRI, CT, and PET modalities that are not available for intraoperative image acquisition can then be used to acquire intraoperative images. Such modalities include OCT, PS-OCT, ultrasound, etc. These modalities will be discussed in more detail below during the discussion of the navigation portion of the surgical procedure.

FIG2 shows an embodiment of the method and system of the present invention, which is used as an intraoperative multimodal surgical planning and navigation system and method. The system and method can be used as a surgical planning and navigation tool during both the preoperative and intraoperative phases. The skilled artisan will appreciate that the surgical planning steps and surgical procedure data inputs described in FIG1 can be used as inputs to the intraoperative navigation phase described in FIG2 by using patient fiducial markers visible in imaging or other imaging techniques (examples of which are known in the art).

The embodiment of Figure 2 provides a user, such as a surgeon, with a unified means of navigating through a surgical area using preoperative data input and updated intraoperative data input. The processor of the system and method is programmed with instructions/algorithms 11 that analyze the preoperative data input and the intraoperative data input to update the surgical plan during the surgical procedure. For example, if the intraoperative input in the form of newly acquired images identifies a previously unknown neural bundle or brain tract, then these inputs can be used to update the surgical plan during surgery to avoid contact with the neural bundle, if necessary. The skilled person will appreciate that the intraoperative input can include a variety of inputs, including local data collected using various sensors.

In some embodiments, the system and method of FIG. 2 may continuously provide updated intraoperative input in the context of a specific surgical procedure with the aid of intraoperative imaging sensors to verify tissue position, update tissue imaging after tumor resection, and update surgical device position during surgery.

The system and method can provide for reformatting of images, for example, to warn of possible penetration of vital structures by surgical tools during surgery or collisions with surgical tools during surgery. In addition, the embodiments disclosed herein can provide imaging and input updates and algorithmic approximations for any offsets that may occur due to needle deflection, tissue deflection, or patient movement to correct for known imaging distortions. The magnitude of these combined errors is clinically significant and can typically exceed 2 cm. Some of the most significant errors are MRI-based distortions, such as gradient nonlinearity errors, magnetic susceptibility offsets, and eddy current artifacts, which can exceed 1 cm on standard MRI scanners (1.5T and 3.0T systems).

The skilled artisan will appreciate that a variety of intraoperative imaging techniques may be implemented to generate intraoperative input, including anatomy-specific MRI devices, surface array MRI scans, intranasal MRI devices, anatomy-specific US scans, intranasal US scans, anatomy-specific CT or PET scans, port-based or probe-based photoacoustic imaging, and optical imaging using remote scanning or probe-based scanning.

FIG3 illustrates an embodiment of the method and system of the present invention for postoperative data analysis. As shown in FIG3 , the input and output 1 captured during the preoperative and intraoperative phases of the method and system described herein can be used for analysis and training purposes for future surgical procedures. The vast amount of data captured during the preoperative and intraoperative phases can be used for future surgical procedures and training purposes.

In such an embodiment, the system may include a dedicated database 2 for storing and retrieving input, output, and processor activity. The database 2 may include data for recovery analysis, outcome assessment, therapy planning, pathology correlation 3, future surgical planning and/or training 4, and cost validation (health outcomes versus economic metrics).

The skilled artisan will appreciate that the inputs and outputs captured by the systems and methods may include data regarding the use of surgical tools, continuous recording of tissue using local imaging scans during the surgical procedure, local Raman spectroscopy, local anisotropy information of the tissue to illustrate morphological structure, local hyperspectral image data of the tissue to assist in tissue differentiation, spatial location of resected tissue associated with specific regions in the body, and pathology information inferred by a pathologist or radiologist to assist in future surgical procedures or for training purposes.

The information accumulated during the preoperative and intraoperative phases can be effectively used for future surgical planning for the same patient, collecting clinically relevant information for preoperative surgical planning of other patients, and/or for training purposes, as shown in FIG3 .

Because the systems and methods disclosed herein can generate large amounts of data to be captured, in some embodiments, input and output data can be communicated to additional system components, for example, to facilitate remote review of the data by a user at a remote location.

In other embodiments, for example, the surgical procedure selected for each patient and clinical criteria may be used as additional input metrics to evaluate the optimal surgical plan. Additional metric inputs may include a minimally invasive trajectory to the location of interest, such as minimized vascular trauma, minimized neural tract trauma, or prioritized neural tract trauma. Metric inputs may include, for example, measured or predicted trauma to brain tissue, including damage to white matter, damage to areas connected by white matter, damage to areas of the cortex connected by white matter, and damage to blood vessels along the approach.

In some embodiments, input metrics may include the angle of contact between tissue and the instrument and trauma to nerves and connected fibers, which may be measured by interception or displacement of tissue by the instrument from both historical and surgical data.

Additional input metrics may include: the position of the device to be tracked by the tracking technology relative to the tissue of interest; the geometry of the surgical device and port; the expected positioning of the instrument and port during surgery; best practice locations for securing the patient's anatomy, such as the headband area for a Mayfield clamp; and locations for associated procedures such as the administration of local anesthetic. The skilled artisan will appreciate that the input metrics may be associated with a specific method, disease, or procedure, and that these metrics may be user-selected or automatically generated.

In further embodiments, the processor can be used to perform corrections for imaging artifacts or anomalies to represent structures and objects in accurate locations. Gradient nonlinearity correction, magnetic susceptibility offset correction, eddy current artifact correction, and pixel interpolation error correction are examples of processes that can be performed by the methods and systems to correct artifacts in images after acquisition and provide high-quality and accurate representations.

In yet other embodiments, the systems and methods may include joint registration components and techniques to align various imaging modalities and scans that vary within a modality. Registration can be performed on images acquired by various types of sensors including MRI, PET, CT, US, optical imaging (e.g., surface scanning and spectroscopic techniques, and acousto-optic imaging).

In yet other embodiments, the systems and methods can be configured to direct the sensor to a specific area of interest within the patient's body to produce high-quality images during surgery that are focused on the specific area of interest, specifically, the area of the desired surgical field or point at the appropriate time during surgery. Such surgical field imaging can be implemented by the system using, for example, appropriate imaging scaling or contrast mechanisms. By focusing imaging on a specific location of interest, the signal-to-noise ratio can be increased many times, and novel imaging contrast mechanisms can be used.

In some embodiments, the systems and methods can generate minimally invasive approaches as output based on processor analysis of input metrics. For example, the input metrics can be weighted to generate patient- or procedure-specific outputs. The processor can rank the various surgical alternatives provided by the surgeon, or the system can automatically generate the various surgical alternatives using adaptive learning paradigms such as decision trees and neural networks.

In some aspects of the methods and systems of the present invention, systems and methods are provided for integrating surgical instrument and port-specific information such as size, shape, or effect on neural tissue with data about the patient's anatomy to qualify a user-selected port approach. For example, inputs including characteristics of the subject's neural fascicles, neural bundles, sulci and gyri patterns, blood vessels, skull, and skull base can be used to assess the effect of surgical instrument or port insertion on the neural structures of the brain. In some embodiments, the systems and methods can aid in surgical instrument and port planning to determine the location of appropriate craniotomies, incisions, head holders, external imaging devices, and equipment in the operating room. These systems and methods can result in less invasive, more accurate, and faster surgical procedures based on insertion of devices or ports, and improved patient and economic outcomes.

In some embodiments, the disclosed systems and methods may include as input data on the fiber, sulcus, and gyrus structure of the brain, in addition to other inputs such as tumor location. These inputs may be used to determine, for example, a path or location for insertion of a surgical device. In some embodiments, the planning output may include a path for insertion of a device into the brain through a natural cavity such as a sulcus. In other embodiments, inputs such as a tumor database may be included in addition to other inputs such as tumor location.

In some embodiments, the systems and methods may include fiber tractography inputs. In the systems and methods described herein, differentiation between tumors and healthy tissue can be performed using a DWI sensor and an associated processor that uses the diffusion of water through brain tissue by Brownian motion as the primary tissue contrast mechanism. Data acquired from a diffusion contrast scan can be acquired with predetermined gradient directions so that diffusion along specific directions in the brain can be visualized as presented in an FA map, which provides information about the general directionality of diffusion throughout the image. The processor can use this directional information to generate a connection map defined by groups of vectors to generate fiber tracts in the brain; wherein these tracts correspond to water diffusion through the brain on the outside of the white matter tracts and correspond to the major nerve fibers in the brain.

For example, the systems and methods may include a diffusion contrast imaging device to generate DTI images and measure fractional anisotropy ("FA") and apparent diffusion coefficient ("ADC") of tissue. ADC (which measures the magnitude of diffusion) and FA (which measures the general directionality of diffusion throughout the image) can be used to identify major fiber tracts through the brain, measure increased cell density associated with tumors, measure diffuse or focal traumatic brain injury, and white matter disease associated with neurodegenerative diseases.

By combining ADC, FA maps, and DTI images, the system and method can measure major fiber tracts passing through the brain, measure increased cell density associated with tumors, measure diffuse or localized traumatic brain injury, and white matter disease associated with neurodegenerative diseases. For example, in order to perform a craniotomy to remove the tumor margins as completely as possible, multiple MRI contrast mechanisms can be used to define the tumor boundaries, define important structures in the brain, define functional areas, and define the approach to tumor resection.

FIG4 shows the output of an embodiment of the method and system of the present invention, wherein the processor has identified fiber bundles for optimal selection of a surgical approach. In the illustrated embodiment, the output may include the location and visualization of transcerebral communication pathways that may aid in avoiding blood vessels and fiber bundles. The output may visualize and track the surgical approach to minimize white matter and gray matter insertion damage.

In some embodiments, the methods and systems disclosed herein may include as input fiber and tissue ordering information.

In some embodiments, the systems and methods of the present invention are configured to identify minimally invasive pathways, such as through cerebral sulci, based on inputs such as the sum of all white matter and gray matter information available to the system, which can be used to calculate minimally invasive pathways. For example, given an MRI T1 image segmented into white matter, gray matter, sulci, and CSF, and surgical entry points and targets specified within the information, the system specifies a grid of image voxel centers and forms 27 connected direct voxel neighbors. Each connection is assigned a weight based on the voxel label as white matter, gray matter, sulci, or other. The weight is selected to reflect the relative priority of influence of one tissue type over other tissue types (which can be determined by the clinician). A pathfinding algorithm (e.g., the A* search algorithm described above) can be used to determine the path with the least overall impact on the tissue. Other embodiments can realistically represent surgical instruments relative to and interacting with the represented tissue, and represent the biomechanical properties of the tissue to simulate tissue deformation as each path is attempted. Other embodiments can integrate additional imaging and outcome information to support clinical decision-making on the approach.

The system and method can generate and plan minimally invasive corridors through several different embodiments, such as: 1) planning without using a deformable model; 2) planning using a deformable model; or 3) planning using intraoperative imaging in the context of a deformable model to update information. An exemplary method for generating a deformable tissue model is disclosed in co-pending PCT patent application serial number PCT/CA2014/050243, entitled "SYSTEM AND METHOD FOR DETECTING TISSUE AND FIBER TRACK DEFORMATION," which is incorporated herein by reference in its entirety.

In one embodiment, the system and method can be configured to operate under the assumption that the tissue does not deform when the port is inserted into the tissue. In this embodiment, the system and method can be configured to generate a minimally invasive access output with a fixed imaging dataset. While this may be a reasonable assumption clinically, during, for example, port surgery, the port will often be routed along a sulcus, which can pull or compress the underlying tissue.

To generate and plan a minimally invasive access, the systems and methods are configured and programmed to select a target of interest, which can be represented as, for example, an overlay of contrast agent uptake information, diffusion-weighted maps (ADCs), T2 changes, or a combination of these and additional contrasts. The target can be, for example, user input, or can be generated by the systems and methods based on existing data or images. When the systems and methods identify a target of interest (e.g., a point on a 3D image set), a representation of the access port selected by the user can be displayed on an output or feedback component of the systems and methods, such as a screen.

For example, during port surgery, the systems and methods can fix the port in place, such as at the tip of a lesion, which can be rotated about that point in three dimensions. The port's line of entry and its axis of insertion define the route taken into the brain, provided the systems and methods select a channel where entry occurs on a single linear trajectory rather than multiple linear or curved trajectories.

The systems and methods disclosed herein can provide for the virtual insertion and removal of ports or other surgical tools into brain tissue. When it is inserted, the aligned set of DTI bundles in contact with the tip and the outer surface of the port can be identified by the system and method through ray tracing or similar calculations. If the fiber contacts the port at an angle of 90 degrees, the system and method can predict that the fiber is at the greatest risk of shearing or tearing due to contact with the port; however, if the fibers extend in parallel, the system and method can detect that they are at the least risk of shearing or tearing. In some embodiments, the system and method can set a threshold (e.g., an angle exceeding 60 degrees) that can indicate damage to the nerve fibers. This threshold can be modified by the surgeon in practice and, when set, can allow all nerve fibers at risk to be inferred during the procedure.

Further embodiments may provide visualization tools to assess the effects of different potential shear angles between the transected fiber and the inserted port. These tools may include the display of a calculated bar graph of the number of fibers at risk of shearing versus the shear cutoff angle. Such a tool may provide information about the sensitivity to this threshold. The embodiment may also be configured to suggest alternative cutoff angles near the set cutoff angle if there is a value where the number of displayed fibers spikes (i.e., there will be a large change in the display given a small change in the cutoff threshold). Alternatively, instead of a binary cutoff threshold, the embodiment may also be configured such that the display can be modulated so that there is a gradient of the displayed fibers (e.g., by reducing fiber strength or increasing transparency) as the intersection angle increases beyond a set threshold or between a minimum set threshold and a maximum set threshold.

In another embodiment, the fiber bundle of embodiment can be shown in Figure 1 and Figure 2. Another embodiment only can show the setting length of each fiber bundle to the intersection of itself and port, rather than the whole path of fiber, because the fiber away from the intersection is less likely to be subject to the impact of port insertion.This length threshold value can be dynamically adjusted and manipulated.This embodiment also can be configured to make the demonstration of each fiber by modulating (for example, by reducing brightness, changing color, increasing transparency or utilizing distance to reduce the fiber thickness of demonstration) to the distance of the port intersection.Alternatively, in other embodiments, the fiber of demonstration can be similarly modulated by the distance to the fiber endpoint with the intersection of port, because than the more distant affected fiber along its track, the affected fiber near the endpoint of fiber may be less affected.

To provide the user with visualization of the neural fibers within the context of the rendered volume, the system can depict the affected neural fibers in black, such that the black lines can be projected, for example, through the 3D rendering. Furthermore, the system can display a thin tile of the rendered DTI data volume, such that the tile can be moved along the axis of the port on an output device to display the affected fibers at various depths along the port. Furthermore, for example, looking coaxially down the port, all fibers contacting the port can be displayed as a rendering on the output device of the systems and methods.

Furthermore, as a means of enhancing the port-based approach, the systems and methods can represent the port as a fixed visualization pattern, such that the brain and tissue beneath the port can be moved relative to the port on an output device or display. This can provide an intuitive way to find the appropriate minimally invasive path to a point of interest.

Additionally, the systems and methods can identify a reference frame at a target point within a target, such as a tumor. This can provide the user with an "inside-out" view that can facilitate visualization of possible pathways to the tumor by identifying openings through the pathway to the surface. This can be used as an alternative or complementary approach to the mapping use of the systems and methods for identifying pathways.

In some embodiments, the systems and methods can model surgical instruments, such as ports, as larger or smaller diameters to determine whether different port sizes can be used for a particular procedure or approach's sensitivity to deviations in the procedure (e.g., misregistration of datasets, inaccurate port navigation, or movement of tissue during the procedure). Additionally, the port target point can be offset by the systems and methods to determine the impact on approach sensitivity.

In some embodiments, in addition to finding the least impactful approach relative to fasciculi and neural bundles, the systems and methods can tend to identify sulci as preferred routes of entry into the brain. In such embodiments, surface mapping of the tissue can be used by the systems and methods to identify these natural cavities. This can constrain the output trajectory to only those that are inserted at the sulci on the brain surface.

In addition, the system can provide an overlay of veins, live gray matter, and arteries presented relative to the approach. Based on this information, the impact of the approach can be better assessed. For example, the system can calculate the total volume or number or length of fiber bundles that can intersect with the port at a given point or along a given trajectory. This can be expressed by the system and method because the total number (e.g., a bar graph) can be weighted to express the hierarchy of predetermined or user-entered neural bundles and fascicles. In some embodiments, the calculation can also be performed by the system and method relative to blood vessels in the brain or relative to major fiber bundles or important tissue banks such as motor strips. In some embodiments, the distance and angle of a surgical device such as a port to the bank can be calculated as an additional metric. As some non-limiting examples, the major fiber bundles to which the system can apply the process may include the corona radiata or the optic chiasm.

In some embodiments, the systems and methods may also use input regarding the general orientation of the fibers relative to the patient frame to weight the fiber bundles. For example, the systems and methods may assign different weights to fibers that may be calculated in the sum of trajectory influences. In some embodiments, the hierarchy of fibers may be weighted by color, such that fibers assigned red will be more important than fibers assigned blue, and fibers assigned blue will be more important than fibers assigned green. In other embodiments, the systems and methods may use colors on the map to define the fiber orientation relative to the port. For example, fibers that are substantially perpendicular to the port may be colored red, while white fibers that are within damage tolerance may be colored blue, and fibers that are outside damage tolerance may be colored green. Alternatively, in some embodiments, the systems and methods may use a partial anisotropy map to represent fiber connectivity, such that the colors attributed to this representation may be scaled to correspond to the weights of the fibers.

In certain embodiments, described system and method can select minimally invasive path, and this path tends to arrive the lesion of being paid attention to along cerebral sulcus and makes cerebral sulcus deform as far as possible least.In such embodiment, described system and method can determine the total distance to cerebral sulcus for given port trajectory, and this distance can for example be expressed as the total distance along port or the total amount of deflection required for port path and cerebral sulcus alignment.When measuring cerebral sulcus approach, the total amount of gray matter or white matter traversed is often the important metric of described system and method.This can be calculated from 3D model by described system and method, and be shown as the measured value by millimeter or other unit meter, or for example be shown as the ratio of gray matter, white matter and cerebral sulcus traversed.In certain embodiments, described system and method can be associated with different weights with different types of tissue (for example, gray matter, white matter and cerebral sulcus) and affected fascicle.This can be calculated from port position, but can utilize additional input measurement in some embodiments, to consider the displacement of cerebral sulcus when port is inserted, and cerebral sulcus follows the outer contour of port.

In some embodiments, the systems and methods can process inputs based on, for example, that the introduction of a surgical access port and introducer tends to displace a significant amount of tissue internally and shift the folds of the cerebral sulci as it is pushed into the brain. For tissue that is stiffer than surrounding brain tissue (e.g., some clots/hematomas, cellular tumors), the systems and methods can take into account the expected internal deflection of the tissue as the introducer is pushed against the tissue. This displacement can be predicted or measured by, for example, the systems and methods using accurate simulations, using a priori tissue stiffness information, geometric knowledge of the introducer and port, a biomechanical model of tissue deformation (using the skull as a boundary condition, the port as a boundary condition), and using preoperative imaging data. In some embodiments, the user can modify many of the variables used for modeling, such as the relative stiffness of the tumor and surrounding tissue, as disclosed in co-pending PCT patent application serial number PCT/CA2014/050243, entitled “SYSTEM AND METHOD FOR DETECTING TISSUE AND FIBER TRACK DEFORMATION,” which is incorporated herein by reference in its entirety.

The user or the implemented systems and methods vary these values, allowing for a visual output related to how the tumor may move within the brain volume, which can provide a good sensitivity analysis for the insertion approach employed. In some embodiments, stiffness can be predicted based on T2, diffusion, and contrast information, but it can also be measured directly from elastography (e.g., ultrasound, MRI, or OCT).

In some embodiments, the system and method can process input and generate output based on the following concept: the sulci in contact with the port can deform the surrounding sulci to match the surface of the port. The system and method can use a biomechanical model to model this interface, wherein the sulci tissue will be at the slip boundary interface with the port. Since diffuse fibers and blood vessels attached to the sulci surface (usually terminating at the end near the brain surface and extending more parallelly lower) tend to track with the sulci, another boundary condition processed by the system and method can be to track fibers using sulci displacement. The network of fibers can then be used as alignment points and act as a connection between a part of the 3D network and its own stress and strain distribution. The global deformation of the brain can be modeled by the continuity of the sulci, blood vessels and major structures using the system and method.

As the introducer is positioned inside the patient (e.g., within the patient's head), the system and method can use real-time imaging information input to update the process and model. In some embodiments, real-time imaging can be performed using an in-situ port. For example, real-time ultrasound imaging performed at the tip of the port can detect tissue stiffness within the brain. This information can be used by the system and method in place of previously predicted stiffness and can provide an estimate of tissue movement. In addition, as the port is introduced into the patient, ultrasound can be used to identify brain sulcus patterns. These actual sulcus patterns can be matched to preoperative sulcus patterns by the system and method, and a deformed preoperative model can be generated based on this information. In this iterative manner, the model will be updated by the system and method based on information obtained during the procedure to provide an accurate representation of tumor location (e.g., modeling of tumor volume within the brain) and the ability to measure the total stress and strain on nerve fibers when the port is inserted into the brain. This can be represented by the system and method as a total value, and like the weights of the fiber layers, the actual strain of the fibers can be used to calculate a value associated with the invasiveness of the surgical approach.

In some embodiments, the systems and methods disclosed herein can be used to better model the movement of a proposed surgical device, such as a port, within a patient's body (e.g., their tissue) to allow removal of tumors that are larger than the opening at the end of the port. In this embodiment, the sweep of the port to touch all boundaries of the tumor can be modeled by the system and method based on the fixation of the port on the surface of the brain. For example, as the port moves through different locations of the tumor, the movement of the port can displace fibers, and a biomechanical model can be used to measure the stress and strain distribution on the fibers in the brain, as previously described. In some embodiments, the systems and methods may include additional strain gauges located outside the port to measure these effects in real time. These values can be associated with a planning model of the brain and indicate to the surgeon when they are inconsistent or when a predetermined tolerance threshold has been exceeded.

In addition, as the port is moved, tissue can be removed in volumes indicated by the surgeon. The biomechanical modeling component of the system and method of the present invention will then calculate the new tissue position using the local volume. Additional real-time imaging can be performed by the system to verify the new tissue boundaries. For example, if real-time imaging with navigation positioning information is available, such images can be compared with the calculated estimated position of the tissue. This comparison can be made directly (if similar comparisons are used in both cases) or in the sense of mutual information (if the data are not directly comparable). The system can then report the quality of the consistency between the new data and the estimated tissue position. Further, in some embodiments, the system and method may include a robot or cyborg manipulator for use in similar contexts. The input to the robot can be strain gauge measurements, which are measured directly in vivo and/or used in conjunction with the stresses and strains predicted in the surgical planning model. The ability of the system and method to measure precise stresses and strains may be useful for surgical interventions involving other brain injuries and diseases such as TBI (traumatic brain injury), Parkinson's disease, multiple sclerosis (MS), and Alzheimer's disease.

In an embodiment, there is a system that includes the following parts: a computer or processing system; preoperative images from various modalities (MRI, CT, PET, etc.); a tracking or navigation system (optional, in the case of a planning system); a single or set of input devices (keyboard, touch screen, mouse, gesture controller, etc.); a single or set of output devices (monitor, laser pointer, etc.); a pointer or tool that acts as a pointing device (optional, in the case of a planning system); a tracked surgical device, such as scissors, ablation device, suction cutter, bipolar device (optional, in the case of a planning system); an external imaging system guided (e.g., automatically, semi-automatically, or manually positioned with alignment feedback) by a tracked access port device and a guide device (to facilitate delivery of external imaging modalities, aligned to deliver imaging through the access port device). The system can be used as a surgical planning system, that is, where intraoperative guidance and intraoperative imaging are not part of the system; or as a combined planning and intraoperative guidance system, where information collected during the surgical procedure is used to guide the next surgical step or measure the expected patient outcome.

In some embodiments, the systems of the present invention may include surgical simulation components, such as robotic systems with tactile feedback. In some embodiments, the simulation features provided by the systems and methods disclosed herein may also be incorporated into a model that can be used for training and planning specific surgical procedures and imaging of the model. U.S. Provisional Patent Applications Serial Nos. 61/900,122 and 61/845,256, both of which are incorporated herein by reference in their entirety, disclose examples of how to create brain models for both imaging and biomechanical training of the brain of a patient undergoing surgery.

Features of the model may include: accurate mapping of the texture of the human brain so that surgical ports can be inserted along the sulci; anatomically correct brain structures to accurately simulate the brain of a specific patient, which can be determined long before surgery by methods such as MRI; simulation of the correct type of tumor and in the correct location on the model (for example, a tumor may be identified a priori as soft and free-flowing or highly cellular and dense. This information can be incorporated into the formation of the simulated brain to accurately match the placement of the tumor to information inferred from preoperative imaging modalities and allow the surgical team to evaluate the specific surgical procedure and approach in the case of a specific patient); simulation of the presence of blood vessels using, for example, colored fluids to simulate the venous structure just below the scalp; and simulation of the presence of skull and dura mater using, for example, a moldable rigid material such as a casting material. The dura mater can be simulated using a polymer sheet that is thin and has a considerable durometer so that the synthetic dura mater is displaced during the surgical opening step. The presence of the synthetic skull allows the surgical team to practice opening the cranial port during the simulation of the craniotomy.

The skilled artisan will appreciate that in all approaches where a quantitative method is used to calculate the trajectory of the port location, an algorithm can be used to calculate a set of ranked trajectory paths from which a user can select. A user, such as a surgeon, can search these options based on different criteria such as minimizing overall fascicle involvement, minimizing vascular involvement, or minimizing total nerve fiber strain.

Furthermore, in some embodiments, once a trajectory is selected, the systems and methods can search a database of previous cases to find similar trajectories used in the context of similar tumor size, location, and DTI tractograms. The results associated with those trajectories can be compared by the systems and methods and presented to influence trajectory selection. In some embodiments, actual intraoperative data can be referenced (e.g., in vivo strain measurements) or postoperative DTI maps.

In use, the systems and methods of the present disclosure can be used for surgical procedures in which it is necessary to avoid important structures that can be imaged using preoperative or intraoperative imaging modes. The surgical planning aspects of the methods and systems of the present invention can be used for minimally invasive access procedures, including: port-based neurosurgical procedures and intranasal approaches such as channel-based procedures, intranasal procedures, port-based procedures (rigid fixed diameter), tumor resection, stroke tissue resection and reperfusion, ICH vascular clipping, biopsy via brain sulcus, stem cell recovery, DBS system delivery, catheter-based (flexible, smaller diameter). Although the systems and methods described herein use port-based surgery and surgical tools as examples, the scope of the invention should not be limited by the embodiments set forth in these examples, but should be given the broadest interpretation consistent with the overall description.

The systems and methods described herein can be used in applications such as spinal surgery procedures, tumor resection, disc repair, tendon alignment, pain management, functional device implantation, neck or sinus surgery, functional surgery, cardiopulmonary surgery, cardiac function, lung cancer removal, removal of clots or diseased tissue, body cancer or colon imaging, polyp removal, liver, prostate, kidney, or pancreas imaging. Those skilled in the art will appreciate that the methods and systems described herein are not limited to the aforementioned uses and surgical procedures, but can be generalized to a variety of procedures utilizing imaging, planning, and navigation.

At least some of the elements of the systems described herein can be implemented by a combination of software or software and hardware. The elements of the system implemented by software can be written in a high-level programming language such as object-oriented programming or scripting language. Accordingly, program code can be written in C, C++, C#SQL or any other suitable programming language, and can include modules or classes, as those skilled in the art of object-oriented programming know. At least some of the elements of the systems implemented by software can be written in assembly language, machine language or firmware as needed. In either case, program code can be stored on a storage medium or a computer-readable medium, and this computer-readable medium can be read out by a general or dedicated programmable computing device, and this computing device has a processor, an operating system and associated hardware and software, and this hardware and software are necessary for implementing at least one function in the embodiments described herein. When read out by a computing device, program code configures the computing device to operate in a new, specific and predetermined manner, so as to perform at least one of the methods described herein.

Thus, although some embodiments have been described in the context of fully functional computers and computer systems, those skilled in the art will appreciate that the various embodiments can be distributed as program products in various forms and can be used without regard to the particular type of machine or computer-readable media used that actually affects distribution.

Computer-readable storage media can be used to store software and data that, when executed by a data processing system, causes the system to perform various methods. Executable software and data can be stored in a variety of places, including, for example, ROM, volatile RAM, non-volatile memory, and/or cache. Portions of the software and/or data can be stored in any of these storage devices. Generally, machine-readable media include any mechanism that provides (i.e., stores and/or transmits) information in a form that can be accessed by a machine (e.g., a computer, a network device, a personal digital assistant, a manufacturing tool, any device with a set of one or more processors, etc.).

6 provides an exemplary, non-limiting implementation of a computer control system 425, which includes: one or more processors 430 (e.g., CPUs/microprocessors); a bus 402; a memory 435, which may include random access memory (RAM) and/or read-only memory (ROM), one or more internal storage devices 440 (e.g., a hard drive, an optical drive, or internal flash memory); a power supply 445; one or more communication interfaces 450; and various input/output devices and/or interfaces 460, such as a user interface for a clinician to provide various inputs, run simulations, etc.

Although only one of each component is shown in FIG6 , the computer control system 425 may include any number of each component. For example, computers typically include multiple different data storage media. Furthermore, although the bus 402 is described as a single connection between all components, it should be understood that the bus 402 may represent one or more circuits, devices, or communication channels that link two or more of the components. For example, in a personal computer, the bus 402 often includes or is the motherboard.

In one embodiment, computer control system 425 may be or include a general-purpose computer or any other hardware equivalent configured for operation in space. Computer control system 425 may also be implemented as one or more physical devices that are coupled to processor 430 via one or more communication channels or interfaces. For example, computer control system 425 may be implemented using an application-specific integrated circuit (ASIC). Alternatively, computer control system 425 may be implemented as a combination of hardware and software, with the software being loaded into the processor from memory or over a network connection.

Examples of computer-readable storage media include, but are not limited to, recordable and non-recordable types of media, such as volatile and non-volatile storage devices, read-only memory (ROM), random access memory (RAM), flash memory devices, floppy disks and other removable disks, magnetic disk storage media, optical storage media (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), etc. The instructions may be embodied in digital and analog communication links for electrical, optical, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, digital signals, etc. The storage medium may be an Internet cloud or a computer-readable storage medium such as a magnetic disk.

Examples of computer-readable storage media include, but are not limited to, recordable and non-recordable types of media, such as volatile and non-volatile memory devices, read-only memory (ROM), random access memory (RAM), flash memory devices, floppy disks and other removable disks, magnetic disk storage media, optical storage media (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), etc. The instructions may be embodied in digital and analog communication links for electrical, optical, acoustical or other forms of propagated signals, such as carrier waves, infrared signals, digital signals, etc.

FIG8 depicts a view that may be provided by a surgical planning system. In this exemplary embodiment, the view includes a 2D slice of a brain volume 800 selected by the user and a virtualized port tool 810 in a specific pose or orientation of the port, with the tip of the port in contact with a target point 820 within the brain. The target may be the location of a lesion within the brain. This embodiment displays a set of beams 830 that are projected to intersect the tool at this orientation. The beams are visually displayed as to whether they intersect the tool in the plane of the current cross-section or within a configurable distance within the cross-section. In the case of port-based neurosurgery, an example of this configurable distance may be 3 mm. The beams are displayed to the user and may include red, green, and blue colors (not shown) to indicate the beam's directionality in three orthogonal directions. When they are within a configurable distance from the intersection with the port, the beams may be displayed as outlines (i.e., without color or opacity). Furthermore, for port-based neurosurgery, this distance may typically be 3 to 10 mm. This configurable distance can be adjusted to take into account the confidence that the surgeon can have in positioning his or her surgical tool relative to the intended location when guided by the surgical navigation system. Thus, this visualization allows the user to understand the DTI bundle intersection information in space around the tool and around the currently visible cross-section (or slice) of the brain 800. When comparing Figure 8 to Figure 9, it is apparent that the number of bundles displayed to the user is smaller in Figure 8 than the number of bundles at different approach angles (or postures) of the same port for the same target point in the brain that are visible in Figure 9. From this, the clinician can infer that the approach of the port tool 810 to the target 820 in Figure 9 will intersect with more bundles than the approach of the tool 810 to the target 820 in Figure 8.

In one embodiment, a clinician can use a patient-specific imaging volume to help him or her select the best entry point into the patient's anatomical structure (e.g., a sulcus in the brain) to reach the tumor. In another embodiment, the clinician can rotate the port tool 810 around a target point 820 located within the brain and employ embodiments of the disclosed systems and methods to score the alternative approaches using predetermined surgical outcome criteria.

In another embodiment, the beam information may be used with a mathematical cost minimization process according to the surgical outcome criteria disclosed herein to automatically suggest an optimal approach to a target 620 location within the patient's anatomy.

FIG9 shows an illustration of the beams intersected by the surgical tool for a different pose relative to the pose used to visualize the intersecting beams in FIG8 . In this case, the pose of the tool is depicted as being out of plane of the 2D slice of the patient volume. The beams are represented using the same rules as described in FIG8 .

Figure 10 shows a 2D cross-sectional visualization of the projected craniotomy extent using a selected trajectory 1000 and surgical tool 810. The craniotomy extent is the size of the skull that is cut to access the brain. Generally, the smaller the size of the incision, the less decompression occurs in the brain, which reduces trauma to the brain. Trajectory 1000 depicts the path along which the tool is inserted. The trajectory can begin at a virtual junction 1040 near the brain surface and end at a target 820. The outwardly extending line 1020 illustrates the available space above the scalp for manipulating the surgical tool 810 during surgery. The radial surface extending within the brain region 1030 illustrates the extent (or volume (as in 3D)) of the brain that will be accessible to the surgical tool for a given size craniotomy. The surgical tool can be moved in this space to visualize the bundle intersection 830 and the volume of the brain region that will be accessible during surgery. In one embodiment, different craniotomy sizes can be selected to evaluate the optimal craniotomy size while also evaluating the area of the brain that will be accessible to the port tool 810 to remove the tissue region of interest. This operation can be performed by a person, or can be automated using a cost minimization algorithm that incorporates the size of the craniotomy and the volume of the accessible area within the brain as constraints. In one embodiment, the minimum volume of the accessible area within the brain can be the volume of the tumor identified within the brain.

Other methods will now occur to those skilled in the art for visualizing a patient imaging volume and overlaying DTI information and displaying a virtual surgical tool of the patient's anatomy relative to a 3D rendering or other 3D image of a 3D sulcal surface map.

In addition, at least some of the methods described herein can be distributed in a computer program product comprising a computer-readable medium carrying computer-usable instructions for execution by one or more processors to perform various aspects of the described methods. The medium can be provided in various forms, such as, but not limited to, one or more magnetic disks, optical disks, tapes, chips, USB keys, external hard drives, wired transmissions, satellite transmissions, Internet transmissions or downloads, magnetic and electronic storage media, digital and analog signals, and the like. The computer-usable instructions can also be in various forms, including compiled and uncompiled code.

While the applicant's teachings are described herein in conjunction with various embodiments for illustrative purposes, it is not intended that the applicant's teachings be limited to these embodiments. Rather, the applicant's teachings described and illustrated herein encompass various alternatives, modifications, and equivalents without departing from the described embodiments, the general scope of which will be defined in the appended claims.

No particular order of steps or phases of the methods or processes described in this disclosure is intended or implied, except to the extent necessary or inherent in the process itself. In many cases, the order of process steps can be varied without changing the purpose, effect, or significance of the described method.

Claims (17)

1. A system for planning brain surgery, comprising: a) A storage device configured to receive and store therein at least one preoperative image dataset of a patient’s brain acquired using at least one imaging modality; b) A computer processor and an associated user interface that communicate with the storage device, the processor being configured to: A 3D volumetric image of a portion of the brain containing one or more potential sulci entry points into the tissue and one or more targets in the brain to be approached is generated from at least one preoperative 3D image dataset, and the image of the 3D volumetric image is stored in the storage device. Input is received through the user interface, and the input includes at least the following list: one or more sulci entry points into the brain, one or more target locations to be approached, a first target location to be approached first, and surgical outcome criteria that will be met by one or more surgical trajectory paths from the one or more sulci entry points to the first of the one or more targets; Upon receiving a list of one or more sulcus entry points, the first target location to be approached, and the surgical outcome criteria, one or more point-by-point surgical trajectory paths are calculated from the one or more sulcus entry points to the first target location, wherein each point-by-point surgical trajectory path passes through one or more associated line points between the one or more sulcus entry points and the first target location to define one or more surgical trajectory paths from the one or more sulcus entry points to the first target location that are consistent with the surgical outcome criteria. The storage device stores the one or more point-by-point surgical trajectory paths and visually displays the one or more point-by-point surgical trajectory paths; and Scores are assigned to the one or more surgical trajectory paths to quantify the degree to which the one or more surgical trajectory paths conform to the surgical outcome criteria. The computer processor is programmed to compare the score of the one or more surgical trajectory paths with the score of the path with the shortest distance between the one or more sulcus entry points and the first target location. Furthermore, wherein the at least one imaging mode is magnetic resonance imaging, which is configured for diffusion tensor imaging to provide fiber tract imaging information, and wherein at least one surgical outcome criterion will be calculated: One or more preferred surgical pathways that do not sever the white matter tract, or If it is not possible to avoid all brain tracts, then cut off one or more preferred surgical channels that cut off as few white matter tracts as possible, or Interruption of one or more preferred surgical pathways in a selected white matter tract chosen by the clinician. The computer processor is programmed with instructions to calculate and display the amount of tissue deformation when the tissue is truncated by a surgical tool during the movement of one or more point-by-point surgical trajectories from the one or more cerebral sulci entry points to the first target location, based on the tissue type and its associated mechanical properties. 2. The system of claim 1, wherein the computer processor is programmed to reformat the 3D image dataset to confirm the location of the one or more targets in the 3D volume and to store the reformatted 3D image data. 3. The system of claim 2, wherein the computer processor is programmed to reformat the 3D image dataset by visualizing the target locations in at least three orthogonal planes to confirm the location of the one or more target locations in 3D space. 4. The system of claim 1, wherein the at least one imaging mode is selected from one of the following list: ultrasound, magnetic resonance imaging, X-ray computed tomography and positron emission tomography. 5. The system of claim 4, wherein the at least one imaging mode is two or more imaging modes, and wherein the computer processor is programmed to jointly register images obtained from the two or more imaging modes. 6. The system of claim 1, wherein the computer processor is programmed with instructions to insert images of surgical tools to be used to approach the one or more target locations into the preoperative 3D image dataset of the patient's brain. 7. The system of claim 6, wherein the entry point is an entry point into the cerebral sulcus, and wherein the computer processor is programmed with instructions such that after the surgical tool is inserted into the specific entry point, the image of the 3D volume of the brain is translated, rotated, or translated and rotated in response to allow visualization of the surgical tool at the specific entry point. 8. The system of claim 7, wherein the computer processor is programmed with instructions to assign different colors to brain tissue types with different structures and functions in the image of the 3D volume, to allow visualization and identification of a specific type of tissue being transected by the surgical tool, and thereby inferring the specific function affected by the tissue transecting using the surgical tool. 9. The system of claim 8, wherein one of the brain tissue types is a brain tract, and wherein the computer processor is programmed with instructions to assign different colors to functionally different brain tracts based on the direction in which the brain tracts extend in the brain and/or the functions they perform. 10. The system of claim 8, wherein the computer processor is programmed with instructions to selectively hide selected portions of the image that are not close to the surgical instrument and/or selectively display those portions of the image that are close to the surgical instrument. 11. The system of claim 10, wherein the computer processor is programmed with instructions to selectively display white matter bundles adjacent to each calculated surgical path and hidden in all other regions of the 3D image of the brain. 12. The system of claim 10, wherein the computer processor is programmed with instructions to selectively display white matter bundles truncated by the surgical instrument along each calculated surgical path. 13. The system of claim 12, wherein one of the inputs relating to the surgical outcome criteria conforming to one or more surgical trajectory paths from the one or more sulcus entry points to the first target location includes selecting those white matter tracts to be truncated and those white matter tracts to be avoided from being truncated. 14. A system for planning brain surgery, comprising: a) A storage device configured to receive and store therein at least one preoperative image dataset of a patient’s brain acquired using at least one imaging modality; b) A computer processor and an associated user interface that communicate with the storage device, the processor being configured to: A 3D volumetric image of a portion of the brain containing one or more potential sulci entry points into the tissue and one or more targets in the brain to be approached is generated from at least one preoperative 3D image dataset, and the image of the 3D volumetric image is stored in the storage device. Input is received through the user interface, and the input includes at least the following list: one or more sulci entry points into the brain, one or more target locations to be approached, a first target location to be approached first, and surgical outcome criteria that will be met by one or more surgical trajectory paths from the one or more sulci entry points to the first of the one or more targets; Upon receiving a list of one or more sulcus entry points, the first target location to be approached, and the surgical outcome criteria, one or more point-by-point surgical trajectory paths are calculated from the one or more sulcus entry points to the first target location, wherein each point-by-point surgical trajectory path passes through one or more associated line points between the one or more sulcus entry points and the first target location to define one or more surgical trajectory paths from the one or more sulcus entry points to the first target location that are consistent with the surgical outcome criteria. The storage device stores the one or more point-by-point surgical trajectory paths and visually displays the one or more point-by-point surgical trajectory paths; and Scores are assigned to the one or more surgical trajectory paths to quantify the degree to which the one or more surgical trajectory paths conform to the surgical outcome criteria. The computer processor is programmed to compare the score of the one or more surgical trajectory paths with the score of the path with the shortest distance between the one or more sulcus entry points and the first target location. Furthermore, wherein the at least one imaging mode is magnetic resonance imaging, which is configured for diffusion tensor imaging to provide fiber tract imaging information, and wherein at least one surgical outcome criterion will be calculated: One or more preferred surgical pathways that do not sever the white matter tract, or If it is not possible to avoid all brain tracts, then cut off one or more preferred surgical channels that cut off as few white matter tracts as possible, or One or more preferred surgical channels for transection of a selected white matter tract chosen by a clinician, wherein the target being approached is a tumor, and wherein the computer processor is programmed with instructions to calculate and display the amount of deformation and/or rolling of the tumor as it is approached and transcribed by a surgical tool, and to calculate and display the deformation and/or rolling of the tumor for multiple approach trajectories. 15. The system of claim 14, wherein the computer processor is programmed with instructions to: once one or more surgical paths have been calculated to meet the surgical outcome criteria, visually display a simulation of the surgical instruments that approach the tumor along the one or more surgical paths and touch all portions of the tumor to achieve tumor resection. 16. The system of claim 15, wherein the computer processor is programmed with instructions to display 3D images of the patient's brain, including the skull layer. 17. The system of claim 16, wherein the computer processor is programmed with instructions to manipulate the surgical tool during the simulation such that the distal end of the surgical tool can reach and remove all portions of the tumor, while the proximal end of the surgical tool maps the path it must follow in order to determine the minimum amount of skull material to be removed during the craniotomy procedure before the surgical tool begins to travel to the tumor.