Difference between revisions of "Non-rigid MR-CT Image Registration"

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[[Image:nonrigid.png |SOME OUTPUTS: Triangular mesh representation of the input segmentation - The first triangular mesh,mapped in the sphere - Spherical representation of the harmonic representation of the input parameterization - RawP colorMap - DistanceMap colorMap(after MANCOVA) and Particle |500px|thumb|right]]
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= Non-rigid Registration of Pre-procedural MRI and Intra-procedural CT for CT-guided Cryoablation Therapy of Liver Cancer Tutorial=
  
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[[Image:nonrigid-cryo.png |SOME OUTPUTS: Triangular mesh representation of the input segmentation - The first triangular mesh,mapped in the sphere - Spherical representation of the harmonic representation of the input parameterization - RawP colorMap - DistanceMap colorMap(after MANCOVA) and Particle |500px|thumb|right]]  
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==Overview==
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This tutorial demonstrates how to perform a pre-operated contrast enhanced MR image to intra-procedure CT image non-rigid registration.
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The case study is CT-guided liver tumor cryoablation.
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[[Image:nonrigid.png |NON-RIGID MR-CT IMAGE REGISTRATION: As shown in this figure, non-rigid registration can enhance visualization of tumor margin and location. |500px|thumb|right]]
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=== Clinical significants ===
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*Non-rigid registrations desirable to compensate for liver deformation caused by patient positioning, respiratory motion, and interventional manipulation.
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*A multi-modality non-rigid registration method can enhance visualization of tumor margins and location during the planning, targeting, and monitoring phases of CT imaging-guided cryoablation procedure.
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[[Image:registrationStrategy.png |REGISTRATION STRATEGY / ROADMAP:  |500px|thumb|right]]  
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=== Tutorial Topics / Targets ===
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* Slicer Modules Used:
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** N4ITKBiasField Correction
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** Editor
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** BRAINSFitIGT Registration
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* Image Processing Tasks Performed:
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** Intensity non-uniformly correction
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** Mask generation / segmentation
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** Inter-modality non-rigid image registration
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* Image Data Used:
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** Pre-operative abdominal MR with surface coil, showing liver
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** Planning and Intra-operative CT with cryo-probe
  
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= Non-rigid MR-CT Image Registration Tutorial=
 
  
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==Overview==
 
  
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Shape analysis has become of increasing interest to the medical community due to its potential to precisely locate morphological changes between healthy and pathological structures. SPHARM-PDM is a tool that computes point-based models using a parametric boundary description for the computing of Shape analysis.
 
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The point-based models computed with the SPHARM-PDM tool can be used in combination with the also UNC designed statistical tool
 
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[[UNC MANCOVA Tutorial| shapeAnalysisMANCOVA]] to perform quantitative morphological assessment of structural changes at specific locations.
 
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* '''Step by step analysis''': With the ShapeAnalysisModule tutorial,you will learn how to load input volumes, run the module to generate triangulated surfaces with inherent correspondences and visualize them thanks to an intuitive quality control. Indeed,compared with previous versions, the ParticleModule is now part of ShapeAnaysisModule.
 
  
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=== Overview ===
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* This tutorial will go through the following steps (in order):
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* Preprocessing
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** MRI mask generation
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** CT mask generation
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** MRI Bias Field Intensity Correction
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* Registration
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** MRI-CT(#1) non-rigid registration
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** CT(#1)-CT(#2) affine registration
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** Resampling & Fusion
  
 
==Tutorials==
 
==Tutorials==
  
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'''Non-rigid MR-CT Image Registration Module tutorial''' : to perform MR-CT and CT-CT image registration [[Media:ShapeAnalysisModule-Tutorial.ppt|‏ [ppt]]][[Media:ShapeAnalysisModule-Tutorial.pdf|‏ [pdf]]]
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'''Non-rigid Registration of Pre-procedural MRI and Intra-procedural CT for CT-guided Cryoablation Therapy of Liver Cancer Tutorial'''  [[Media:Non-rigid_Registration_of_Pre-procedural_MRI_and_Intra-procedural_CT_for_CT-guided_Cryoablation_Therapy_of_Liver_Cancer_TutorialContestSummer2011.ppt‎|‏ [ppt]]][[Media:Non-rigid_Registration_of_Pre-procedural_MRI_and_Intra-procedural_CT_for_CT-guided_Cryoablation_Therapy_of_Liver_Cancer_TutorialContestSummer2011.pdf|‏ [pdf]]]
  
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==Tutorial materials==
  
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==Tutorial materials==
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'''Material data''':
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*[[Media:Non-rigid_MR_CT_Image_RegistrationTutorialData_TutorialContestSummer2011.tar.gz| Non-rigid_MR_CT_Image_RegistrationTutorialData_TutorialContestSummer2011.tar.gz ]]
  
  
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'''ShapeAnalysisModule data ''':
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==Reference==  
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*[http://www.nitrc.org/docman/index.php?group_id=308&selected_doc_group_id=760&language_id=1#folder Link to NITRC ]
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This method is based on the registration workflow originally developed for prostate MRI registration as follows.
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* or directly here :[[Media:ShapeAnalysisModuleTutorialData_TutorialContestSummer2011.zip|‏ ShapeAnalysisModuleTutorialData_TutorialContestSummer2011.zip]]
 
  
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A. Fedorov, K. Tuncali, F. Fennessy, J. Tokuda, N. Hata, S.W. Wells, R. Kikinis, and C.M. Tempany. Hierarchical Image Registration for Improved Sampling during 3T MRI-guided Transperineal Targeted Prostate Biopsy, ISMRM 2011
  
 
==People==
 
==People==
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Atsushi Yamada  
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Atsushi Yamada, Ph.D. (Research Associate, Brigham and Women's Hospital and Harvard Medical School)
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<br>Dominik S. Meier
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<br>Dominik S. Meier, Ph.D. (Assistant Professor, Brigham and Women's Hospital and Harvard Medical School)
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<br>Nobuhiko Hata
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<br>Nobuhiko Hata, Ph.D. (Associate Professor, Brigham and Women's Hospital and Harvard Medical School)
  
  
 
==[http://wiki.na-mic.org/Wiki/index.php/Summer_2011_Tutorial_Contest Back to tutorial contest]==
 
==[http://wiki.na-mic.org/Wiki/index.php/Summer_2011_Tutorial_Contest Back to tutorial contest]==

Latest revision as of 21:52, 22 June 2011

Home < Non-rigid MR-CT Image Registration

Non-rigid Registration of Pre-procedural MRI and Intra-procedural CT for CT-guided Cryoablation Therapy of Liver Cancer Tutorial

Overview

This tutorial demonstrates how to perform a pre-operated contrast enhanced MR image to intra-procedure CT image non-rigid registration. The case study is CT-guided liver tumor cryoablation.

NON-RIGID MR-CT IMAGE REGISTRATION: As shown in this figure, non-rigid registration can enhance visualization of tumor margin and location.

Clinical significants

  • Non-rigid registrations desirable to compensate for liver deformation caused by patient positioning, respiratory motion, and interventional manipulation.
  • A multi-modality non-rigid registration method can enhance visualization of tumor margins and location during the planning, targeting, and monitoring phases of CT imaging-guided cryoablation procedure.
REGISTRATION STRATEGY / ROADMAP:

Tutorial Topics / Targets

  • Slicer Modules Used:
    • N4ITKBiasField Correction
    • Editor
    • BRAINSFitIGT Registration
  • Image Processing Tasks Performed:
    • Intensity non-uniformly correction
    • Mask generation / segmentation
    • Inter-modality non-rigid image registration
  • Image Data Used:
    • Pre-operative abdominal MR with surface coil, showing liver
    • Planning and Intra-operative CT with cryo-probe



Overview

  • This tutorial will go through the following steps (in order):
  • Preprocessing
    • MRI mask generation
    • CT mask generation
    • MRI Bias Field Intensity Correction
  • Registration
    • MRI-CT(#1) non-rigid registration
    • CT(#1)-CT(#2) affine registration
    • Resampling & Fusion

Tutorials

Non-rigid Registration of Pre-procedural MRI and Intra-procedural CT for CT-guided Cryoablation Therapy of Liver Cancer Tutorial ‏ [ppt]‏ [pdf]

Tutorial materials

Material data:


Reference

This method is based on the registration workflow originally developed for prostate MRI registration as follows.

A. Fedorov, K. Tuncali, F. Fennessy, J. Tokuda, N. Hata, S.W. Wells, R. Kikinis, and C.M. Tempany. Hierarchical Image Registration for Improved Sampling during 3T MRI-guided Transperineal Targeted Prostate Biopsy, ISMRM 2011

People

Atsushi Yamada, Ph.D. (Research Associate, Brigham and Women's Hospital and Harvard Medical School)
Dominik S. Meier, Ph.D. (Assistant Professor, Brigham and Women's Hospital and Harvard Medical School)
Nobuhiko Hata, Ph.D. (Associate Professor, Brigham and Women's Hospital and Harvard Medical School)


Back to tutorial contest

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