HK1189341A - System and method for treatment planning of organ disease at the functional and anatomical levels - Google Patents

Abstract

The present inventive disclosure generally relates to a system and method for planning the treatment of soft organs utilizing CT and SPECT image technology. The systems and method combine the segmented CT images with the SPECT image to form a combined image and treatment plan utilizing the images of both systems.

Description

System and method for therapy planning of organ disease at the functional and anatomical level

Cross Reference to Related Applications

This application claims priority to U.S. provisional application No. 61/430,458, filed on 6/1/2011, the contents of which are incorporated herein in their entirety by this reference.

Technical Field

The present teachings relate to systems and methods for therapy planning for soft organ disease in medical imaging. More particularly, the present teachings relate to methods and systems for therapy planning that combine functional and anatomical information in SPECT and CT images.

Background

Computerized tomography or CT modalities are widely used for diagnostic and therapeutic follow-up purposes. It can provide very detailed anatomical information on human organs. For example, in oncology, CT is used to monitor the therapeutic response to treatment of tumors by measuring changes in tumor size. However, the reflection of therapy on the size change of the tumor may take a long time, e.g., several weeks. SPECT (single photon emission computed tomography) is a method that can provide direct metabolic measurements of human organs and/or tumors, allowing one to functionally differentiate healthy from diseased tissue. While both SPECT and CT provide imaging information, the imaging obtained from SPECT does not provide detailed anatomical information about human organs in the same manner as CT does. Furthermore, SPECT images are much lower resolution than CT, so it may not be possible for accurate therapy planning to rely solely on SPECT images. More recently, CT and SPECT are combined in one imaging device, allowing both CT and SPECT images to be referenced across each other. Currently, the use of SPECT-CT imaging is utilized primarily at the image level. For example, SPECT and CT images may be overlaid on each other for cross-reference. In percutaneous treatment of e.g. liver lesions, e.g. by radiofrequency ablation (RFA) or chemoembolization, it is desirable to plan the treatment based on anatomical and functional information. Furthermore, the functional and anatomical images can be combined for more accurate segmentation of liver lesions.

Therefore, combining anatomical information from CT for region analysis with functional analysis of SPECT at both anatomical and functional levels is highly desirable for therapy planning and therapy monitoring.

SUMMARY

In an embodiment of the present disclosure, a system for therapy planning for soft organs is disclosed. The system comprises a first device for generating a functional image of the soft organ, a second device for generating a structural image of the soft organ, a dividing unit for dividing the structural image of the soft organ, a geometric transformation unit, a functional dividing unit, a fusion unit for combining the functional division of the soft organ with the structural division of the soft organ, and a treatment planning unit.

In another embodiment, the soft organ is a liver. In another embodiment, the first device is a single photon emission computed tomography device. In another embodiment, the second device is a computerized axial tomography (CT) device. In another embodiment, the structural imaging system further comprises an organ vessel delineation unit. In another embodiment, the system further comprises a first device-second device lesion segmentation unit. In a further embodiment, the organ vessel segmentation unit generates a vessel image, wherein the vessel image is combined with the functional image from the first device.

In another embodiment, a method implemented on a machine having at least one processor, memory, and a communication platform connected to a network for formulating a treatment plan for a soft organ is disclosed. The method includes dividing a soft organ image obtained from an imaging device in a first image space, mapping the divided images into a second image space, defining a soft organ boundary in the second image space, dividing a defective organ region within the soft organ boundary in the second image space, mapping the identified defective region divided in the second image space to the soft organ image obtained in the first image space, dividing a target based on the mapped defective region and anatomical information in the first image space, dividing other structures based on the soft organ image in the first image space, combining the divided images in the first image space and the second image space into a combined image, and generating a treatment plan based on the combined image.

In another embodiment, the soft organ is a liver. In another embodiment, the target is a tumor. In yet another embodiment, the first image space is a CT image. In yet another embodiment, the second image space is a SPECT image. In a further embodiment, the definition of the liver boundary in the second image space is based on the image obtained in the first image space. In another embodiment, the partitioning of the defective organ region is based on a threshold metric. In another embodiment, the partitioning of the target object is based on an optimization that maximizes the combined gradient of the image from the first image space and the mapped image from the second image space and minimizes intensity variations within the target object. In another embodiment, the other structure is a vascular structure. In another embodiment, the target is a tumor. In another embodiment, the combined image from the first image space and the mapped image from the second image space are a CT image and a mapped SPECT image.

In yet another embodiment, a computer readable tangible and non-transitory medium having information recorded thereon for forming a treatment plan for soft organ disease is disclosed. Wherein the information, when read by the machine, causes the machine to divide a soft organ image derived from the imaging device in a first image space, map the divided image into a second image space, define a soft organ boundary in the second image space, divide a defective organ region within the soft organ boundary in the second image space, map the identified defective region divided in the second image space to the soft organ image derived in the first image space, divide the target based on the mapped defective region and anatomical information in the first image space, divide other structures based on the soft organ image in the first image space, combine the divided images in the first and second image spaces into a combined image, and generate a treatment plan based on the combined image. In another embodiment, the soft organ is a liver. In another embodiment, the target is a tumor.

Brief description of the drawings

The invention as claimed and/or described herein is further described in terms of embodiments. These embodiments are described in detail with reference to the accompanying drawings. These embodiments are non-limiting embodiments in which like numerals represent like structures throughout the several views of the drawings, and in which:

figure 1 illustrates a system diagram of functional and anatomical image based treatment planning and follow-up job assessment according to the present disclosure;

FIG. 2 depicts a flow diagram of treatment planning and follow-up work evaluation based on functional and anatomical images;

FIG. 3 is an example of a mapping of anatomical analysis results in CT to SPECT according to the present disclosure;

fig. 4 depicts a flow diagram of mixed lesion segmentation based on functional and structural information in accordance with the present disclosure;

FIG. 5 depicts a composition of a combination of structural information in CT and functional information in SPECT according to the present disclosure; and

FIG. 6 depicts a computer system for implementing the systems and methods of the present disclosure, according to the present disclosure.

Detailed Description

The present teachings disclose herein systems and methods for therapy planning and follow-up work for soft organ therapy using functional and anatomical information. The disclosure herein refers to systems and methods for treatment planning and follow-up work on liver lesions using functional and anatomical information. The disclosed embodiments refer to the liver as the soft organ under study. However, it should be understood that treatment planning, follow-up, surgery, and other procedures on any organ, such as the heart, lungs, kidneys, stomach, intestines, brain, or other soft organs, may utilize and benefit from the present disclosure. Thus, for ease of clarity, reference to the liver is used to describe embodiments of the systems and methods of the present disclosure, but this is not a limitation, as will be appreciated by those skilled in the art, and does not limit the scope of the invention in any way. The illustrated example is based on SPECT-CT images. Other functional modalities such as PET (positron emission tomography) can also be used in a similar manner. Furthermore, other methods of providing anatomical information, such as MR, may be used in place of CT for the present teachings.

Fig. 1 illustrates an exemplary system diagram of a soft organ treatment planning and monitoring system 100. The system can be composed of a CT organ or liver dividing unit 106, a CT-to-SPECT geometric conversion unit 108, a SPECT function deficient liver or soft organ region dividing unit 112, a SPECT-to-CT geometric conversion unit 114, a hybrid SPECT-CT lesion dividing unit 118, a CT vascular structure dividing unit 116, a SPECT-CT fusion unit 119, and a treatment planning and follow-up work evaluation unit 120. The CT-partitioned liver derived by element 106 is mapped to SPECT space by element 108 based on the CT-SPECT geometric transformation parameters. In the SPECT image space, the CT liver region is used to bound the partition of the functionally defective liver region by unit 112. The functionally defective liver regions in SPECT are combined with the CT image to get a more accurate lesion segmentation by unit 118. The divided lesion together with the functionally defective region from SPECT is fused with other anatomical structures, e.g. vascular structures resulting from unit 116, by unit 119. The therapy planning and follow-up assessment unit 120 combines the functional analysis results in SPECT with the structural analysis results in CT to make a therapy plan or to derive an assessment of the therapy that has been performed.

Fig. 2 illustrates an exemplary flow diagram of fig. 1 in accordance with the present disclosure. First, in step 202, soft organs, such as liver organs, are segmented in CT by any method known in the art, such as the method disclosed in U.S. application No. 11/474505, "Methods for interactive liver disease diagnosis," to Guo-Qing Wei et al. In step 204, the segmented liver may be mapped into SPECT image space. The mapping may be based on SPECT-CT mechanical registration parameters or on known registration methods, such as D.Rueckert, L.Sonoda, C.Hayes, D.Hill, M.Leach and D.Hawkes "non-linear registration using from-to-forms" (IEEE trans. Med.Imag, Vol.18, No.8, pp.712-721,1999, 8 months), which may take into account respiratory and body motion effects between SPECT and CT images.

The mapped liver is used to define a liver boundary in the SPECT image at step 206. Since functionally defective liver regions, such as lesions, do not show metabolic activity in SPECT, the intensity of such regions is similar to background, i.e., no illumination. Thus, the CT liver boundary mapped into SPECT space helps define the liver boundary in SPECT. Since the liver boundary is defined in SPECT, organ or liver regions that are functionally defective may be demarcated within the organ or liver boundary at step 208. Segmentation may be performed by thresholding or other advanced partitioning methods such as level sets in "Shape modeling with front prediction: A level set approach" (IEEEtransactions on Pattern Analysis and Machine Analysis 17,2(1995), 158) of Malladi, R.Sethian, J.A., and Vemuri, B. At step 210, the partitioned functionally defective regions may be mapped to a CT image based on SPECT-CT geometric transformation parameters. In step 212, the liver region may be partitioned based on the functional and anatomical information. The partitioning results can be adjusted interactively and manually by the operator. The adjustment may be any method of editing the image, such as cutting or patching of the division results or adjustment of the division parameters.

At step 214, the vascular structure may be delineated in the CT using known methods. At step 216, the SPECT and CT information may be fused or combined into a unified image. In embodiments of the present teachings, the fusion may be in the form of 3D object visualization, e.g., displaying functionally defective SPECT areas, compartmentalized lesions, compartmentalized organs or liver and vessel structures in the same 3D space. It may also be in the form of a functionally defective SPECT region and a compartmentalized lesion superimposed on the SPECT and CT images. At step 218, the treatment is planned or evaluated. In an embodiment, during therapy planning, based on the mixed lesion segmentation result, a virtual cut may be performed based on the spatial relationship of the segmented lesion to the vessel structure. Additionally or alternatively, in percutaneous treatment of liver lesions, an ablation region may be defined based on a mixed segmentation result. In one embodiment, a safety boundary may be defined that specifies the distance of the virtual incision or ablation region to the surface of the hybrid compartmentalized lesion. The function of the remaining liver can be calculated based on the isotope counts of SPECT images of the remaining liver. In one embodiment, the user may adjust the security boundary size through a user interface. As a result of the adjustment, the function of the residual liver may be calculated and displayed to the user. In another embodiment, different virtual cutting or ablation plans may be selected by the user, and the residual liver function of the respective plan may be calculated and displayed to the user in different formats for decision-making. In an embodiment, the size of a lesion segmented from the mixed segmentation may be compared to the size of the same segmentation of the SPECT-CT image at a previous time during a subsequent session of treatment.

Fig. 3 depicts an example of an embodiment of a mapping between SPECT and CT image spaces of a liver segment. Fig. 3 (a) is a slice image of a CT liver image sequence, wherein region 302 indicates a diseased liver region. Fig. 3 (b) a slice image of a SPECT image at the same location of the liver organ as the CT image, where 304 shows the liver boundary from the CT image mapped onto the SPECT image space. As explained above, the dark regions represent diseased regions that do not show uptake in SPECT. Fig. 3 (c) shows the result of the division of a functionally defective region 305 in SPECT based on the SPECT image intensity and liver boundary of fig. 3 (b).

Fig. 4 depicts steps and flow in an embodiment of mixed lesion segmentation. In step 402, the intensity gradient of the SPECT image is computed for the functionally defective region. In step 404, an intensity gradient of the CT image is calculated. At step 406, a mixed gradient map is constructed based on the weighted sum of the SPECT gradients and the CT gradients. The weights of the SPECT and CT gradient images may be computed as the inverse of the maximum gradient values of the SPECT and CT gradient images, respectively. This serves as a normalization of the gradient values. At step 408, the CT image intensities are organized into raw CT values and weighted values for functionally defective regions. At step 410, a region is determined that maximizes the gradient at the boundary of the region while minimizing the variation in weighted CT intensity within the region.

Fig. 5 illustrates an embodiment of using SPECT for therapy planning based on functional analysis of the liver in SPECT and anatomical analysis in CT. Fig. 5 (a) shows a lesion 502 in the liver 503 in CT image space. Fig. 5 (b) shows planned resection of a lesion only in CT, with the resection plane indicated by 504. This would be the cutting plane when no SPECT image is available. Fig. 5 (c) shows a functional diagram of the liver in SPECT, where marker 509 indicates healthy liver tissue and marker 508 indicates diseased liver tissue. The dashed curve 506 indicates the ablation plane mapped from the CT space. It can be seen that the resection plane does not completely cover the functionally defective area 508. This means that anatomical information based on CT only does not completely reflect functional information of the liver. In fig. 5 (d), a region 510 indicates a lesion region divided from a mixed division. Based on the functional information in SPECT and the anatomical information in CT, a modified ablation plane can be derived, such as curve 512, which now includes all functionally defective regions and lesions.

FIG. 6 shows a functional block diagram illustration of a general computer architecture on which the present teachings may be implemented, and having a computer hardware platform that includes user interface elements. The computer may be a general purpose computer or a special purpose computer. The computer 600 may be used for any component that performs functional analysis using SPECT-CT imaging as described herein. For example, image display, image storage, image processing may be implemented on a computer such as computer 600 via its hardware, software programs, firmware, or a combination thereof. While only one such computer is shown for convenience, the computer functions related to the present disclosure described herein may be implemented in a distributed manner on many similar platforms to distribute processing load.

The computer 600 includes, for example, COM ports 605 connected to and from a network to facilitate data communications. The computer 600 also includes a Central Processing Unit (CPU) 620 in the form of one or more processors for executing program instructions. The exemplary computer platform includes an internal communication bus 610, program storage and different forms of memory, such as a disk 670, Read Only Memory (ROM) 630 or Random Access Memory (RAM) 640, for causing various data files to be processed and/or transferred by the computer and possibly program instructions to be executed by the CPU. The computer 600 also includes I/O components 660 that support the flow of input/output data between the computer and other components therein, such as user interface elements 680. The computer 600 may also receive programming and data via network communications.

Thus, aspects of therapy planning in SPECT-CT images as outlined above may be embodied in programming. The procedural aspects of the present technology may be considered an "article of manufacture" or an "article of manufacture" typically in the form of executable code and/or related data carried on or embodied in a type of machine-readable medium. Tangible, non-transitory "storage" type media include any or all of the memory of a computer, processor, etc. or its associated modules, or other storage devices that can provide storage of software programming at any time, such as various semiconductor memories, tape drives, disk drives, etc.

All or portions of the software may sometimes be in communication over a network, such as the internet or various other communication networks. Such communication may, for example, enable loading of software from one computer or processor to another computer or processor. Another type of media that can carry software elements includes optical, electrical, and electromagnetic waves, for example, those used in the physical interface between local devices through wired and optical land-line networks and through various air links. Physical elements carrying such waves, such as wired or wireless links, optical links, etc., may also be considered a medium carrying software. As used herein, unless limited to a tangible "storage" medium, terms such as a computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

Thus, a machine-readable medium may take many forms, including but not limited to, tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, any storage device such as in any computer or the like, which may be used to implement a system as shown in the figures or any of its components. Various storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that form the bus within a computer system. Carrier-wave transmission media can take the form of electrical or electromagnetic signals, acoustic or light waves, such as those generated during Radio Frequency (RF) and Infrared (IR) data communications. Common forms of computer-readable media therefore include, for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which computer-readable programming code and/or data can be retrieved. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

Those skilled in the art will recognize that the present teachings are amenable to various modifications and/or enhancements. For example, although the implementation of the various components described above may be embodied in a hardware device, it may also be implemented as a software-only solution. Further, functional analysis utilizing SPECT-CT images as discussed herein may be implemented as firmware, a firmware/software combination, a firmware/hardware combination, or a hardware/firmware/software combination. While the invention has been described with reference to certain illustrated embodiments, the words used herein are words of description, rather than words of limitation. Changes may be made, within the purview of the appended claims, without departing from the scope and spirit of the invention in its aspects. Although the present disclosure has been described herein with reference to particular structures, acts, and materials, the present disclosure is not to be limited to the particulars disclosed, but rather can be embodied in a wide variety of forms, some of which may be quite different from those of the disclosed embodiments, and extends to all equivalent structures, acts, and materials, such as are within the scope of the appended claims.

Claims (22)

1. A system for therapy planning for a soft organ, comprising:

a first device for generating a functional image of a soft organ;

a second device for generating a structural image of the soft organ;

a dividing unit for dividing the structural image of a soft organ;

a geometry conversion unit;

a function dividing unit;

a fusion unit for combining functional division of the soft organ with structural division of the soft organ; and

a treatment planning unit.

2. The system of claim 1, wherein the soft organ is a liver.

3. The system of claim 2, wherein the first device is a single photon emission computed tomography device.

4. The system of claim 2, wherein the second device is a computerized axial tomography (CT) device.

5. The system of claim 2, wherein the second device further comprises an organ vessel delineation unit.

6. The system of claim 2, further comprising a first device-second device lesion segmentation unit.

7. The system of claim 5, wherein the organ vessel segmentation unit generates a vessel image, and wherein the vessel image is combined with a functional image from the first device.

8. A method implemented on a machine having at least one processor, memory, and a communication platform connected to a network for formulating a treatment plan for a soft organ, comprising:

dividing a soft organ image obtained from an imaging device in a first image space;

mapping the divided image into a second image space;

defining a soft organ boundary in the second image space;

dividing a defective organ region within the soft organ boundary in the second image space;

mapping the identified defective regions divided in the second image space to the soft organ image obtained in the first image space;

demarcating a target object based on the mapped defective region and anatomical information in the first image space;

demarcating other structures based on the soft organ image in the first image space;

combining the divided images in the first image space and the second image space into a combined image;

generating one or more treatment plans based on the combined image; and

residual organ function is calculated for each treatment plan.

9. The method of claim 8, wherein the soft organ is a liver.

10. The method of claim 8, wherein the first image space is a CT image.

11. The method of claim 8, wherein the second image space is a SPECT image.

12. The method of claim 9, wherein the definition of the liver boundary in the second image space is based on an image obtained in the first image space.

13. The method of claim 8, wherein the partitioning of the defective organ region is based on a threshold metric.

14. The method of claim 8, wherein the partitioning of the target object is based on an optimization that maximizes a combined gradient of the image from the first image space and the mapped image from the second image space and minimizes intensity variations within the target object.

15. The method of claim 8, wherein the other structure is a vascular structure.

16. The method of claim 8, wherein the target is a tumor.

17. The method of claim 14, wherein the combined image from the first image space and the mapped image from the second image space are CT and mapped SPECT images.

18. The method of claim 8, further comprising:

a safety boundary specifying a distance of the virtual incision to a surface of the mixed compartmentalized lesion is defined.

19. The method of claim 18, wherein the security boundary is defined using a user-adjustable interface.

20. A computer readable tangible and non-transitory medium having information recorded thereon for forming a treatment plan for soft organ disease, wherein the information, when read by a machine, causes the machine to perform the following operations:

dividing a soft organ image obtained from an imaging device in a first image space;

mapping the divided image into a second image space;

defining a soft organ boundary in the second image space;

dividing a defective organ region within the soft organ boundary in the second image space;

mapping the identified defective regions divided in the second image space to the soft organ image obtained in the first image space;

demarcating a target object based on the mapped defective region and anatomical information in the first image space;

demarcating other structures based on the soft organ image in the first image space,

combining the divided images in the first image space and the second image space into a combined image;

generating a treatment plan based on the combined image; and

residual organ function is calculated for each treatment plan.

21. The medium of claim 20, wherein the soft organ is a liver.

22. The medium of claim 20, wherein the target is a tumor.