[go: up one dir, main page]

Torrado‐Carvajal et al., 2016 - Google Patents

Multi‐atlas and label fusion approach for patient‐specific MRI based skull estimation

Torrado‐Carvajal et al., 2016

View PDF
Document ID
1433279726630717075
Author
Torrado‐Carvajal A
Herraiz J
Hernandez‐Tamames J
San Jose‐Estepar R
Eryaman Y
Rozenholc Y
Adalsteinsson E
Wald L
Malpica N
Publication year
Publication venue
Magnetic resonance in medicine

External Links

Snippet

Purpose MRI‐based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1‐weighted volume. Methods The skull is estimated using a …
Continue reading at hal.science (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves involving electronic or nuclear magnetic resonance, e.g. magnetic resonance imaging

Similar Documents

Publication Publication Date Title
Torrado‐Carvajal et al. Multi‐atlas and label fusion approach for patient‐specific MRI based skull estimation
Huynh et al. Estimating CT image from MRI data using structured random forest and auto-context model
Reinhold et al. Evaluating the impact of intensity normalization on MR image synthesis
Kazemi et al. Quantitative comparison of SPM, FSL, and brainsuite for brain MR image segmentation
Pedoia et al. Fully automatic analysis of the knee articular cartilage T1ρ relaxation time using voxel‐based relaxometry
Johansson et al. CT substitute derived from MRI sequences with ultrashort echo time
Juttukonda et al. MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units
Schuh et al. Unbiased construction of a temporally consistent morphological atlas of neonatal brain development
Bezrukov et al. MR-based PET attenuation correction for PET/MR imaging
Wollenweber et al. Evaluation of an atlas-based PET head attenuation correction using PET/CT & MR patient data
Yang et al. MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning
Su et al. Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering
Gudur et al. A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning
Johansson et al. Voxel‐wise uncertainty in CT substitute derived from MRI
Kang et al. Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [18F] FDG PET images
Chen et al. Localization and segmentation of 3D intervertebral discs in MR images by data driven estimation
Yang et al. Pseudo CT estimation from MRI using patch-based random forest
Khalifé et al. Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?
Izquierdo-Garcia et al. Magnetic resonance imaging-guided attenuation correction of positron emission tomography data in PET/MRI
Arabi et al. Whole‐body bone segmentation from MRI for PET/MRI attenuation correction using shape‐based averaging
Poynton et al. Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners
Khateri et al. Generation of a four-class attenuation map for MRI-based attenuation correction of PET data in the head area using a novel combination of STE/Dixon-MRI and FCM clustering
Crombé et al. Assessment of repeatability, reproducibility, and performances of T2 mapping‐based radiomics features: a comparative study
Mérida et al. Evaluation of several multi-atlas methods for PSEUDO-CT generation in brain MRI-PET attenuation correction
Chen et al. SC-GAN: Structure-completion generative adversarial network for synthetic CT generation from MR images with truncated anatomy