Difference between revisions of "Projects:NonRigidEPIRegistration"
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| − | + | Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MGH|MGH Algorithms]], [[Engineering:Kitware|Kitware Engineering]], [[DBP1:Dartmouth|Dartmouth DBP 1]] | |
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= NonRigid EPI Registration = | = NonRigid EPI Registration = | ||
| − | Our Objective is to identify optimal ITK method and parameter settings for non-rigid intrasubject registration of T2 EPI, the raw building block images of DTI, to T1 conventional images. Provide software | + | Our Objective is to identify optimal ITK method and parameter settings for non-rigid intrasubject registration of T2 EPI, the raw building block images of DTI, to T1 conventional images. Provide software deliverable. |
= Description = | = Description = | ||
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''Background'' The registration project aims to leverage one of the general deformable registration algorithms available in ITK for use in developing a specific method to achieve intra-subject, multi-modality (EPI to T1) registration that's demonstrably better than that produced by affine tools (FLIRT, Slicer). We've developed a working tool based on ITK's BSpline method with results that are promising based on visual inspection, and identified some needed fixes (e.g. for partial brain registrations). We have a couple of several-subject datasets with DTI and MPRAGE acquisitions for each subject at two separate time points. We've initiated a study to assess robustness of affine registrations of DTI->MPRAGE and compare to robustness of our BSpline tool. This will also help drive the fixing/optimization of our tool. | ''Background'' The registration project aims to leverage one of the general deformable registration algorithms available in ITK for use in developing a specific method to achieve intra-subject, multi-modality (EPI to T1) registration that's demonstrably better than that produced by affine tools (FLIRT, Slicer). We've developed a working tool based on ITK's BSpline method with results that are promising based on visual inspection, and identified some needed fixes (e.g. for partial brain registrations). We have a couple of several-subject datasets with DTI and MPRAGE acquisitions for each subject at two separate time points. We've initiated a study to assess robustness of affine registrations of DTI->MPRAGE and compare to robustness of our BSpline tool. This will also help drive the fixing/optimization of our tool. | ||
| − | We have developed the requirements specification, and created the Wiki project page and NAMIC sandbox. Test images have been identified and transfered from the 6 healthy individuals from the Dartmouth data set. We have implemented an initial rigid registration method, for comparison purposes. We also implemented the deformable registration method. Current efforts are underway to write the | + | We have developed the requirements specification, and created the Wiki project page and NAMIC sandbox. Test images have been identified and transfered from the 6 healthy individuals from the Dartmouth data set. We have implemented an initial rigid registration method, for comparison purposes. We also implemented the deformable registration method. Current efforts are underway to write the requsite test engine and develop the evaluation strategy. |
= Key Investigators = | = Key Investigators = | ||
| − | + | * MGH Algorithms: Dave Tuch, Denis Jen, Josh Snyder | |
| − | * MGH: Dave Tuch, Denis Jen, Josh Snyder | + | * Kitware Engineering: Luis Ibanez |
| − | * Dartmouth: Andrew Saykin | + | * Dartmouth DBP 1: Andrew Saykin |
| − | + | Project Week Results: [[Engineering:Project:Non-rigid_EPI_registration|Jan 2006]] | |
| − | + | [[Category: Registration]] [[Category:Diffusion MRI]] | |
Latest revision as of 15:25, 21 August 2009
Home < Projects:NonRigidEPIRegistrationBack to NA-MIC Collaborations, MGH Algorithms, Kitware Engineering, Dartmouth DBP 1
NonRigid EPI Registration
Our Objective is to identify optimal ITK method and parameter settings for non-rigid intrasubject registration of T2 EPI, the raw building block images of DTI, to T1 conventional images. Provide software deliverable.
Description
Background The registration project aims to leverage one of the general deformable registration algorithms available in ITK for use in developing a specific method to achieve intra-subject, multi-modality (EPI to T1) registration that's demonstrably better than that produced by affine tools (FLIRT, Slicer). We've developed a working tool based on ITK's BSpline method with results that are promising based on visual inspection, and identified some needed fixes (e.g. for partial brain registrations). We have a couple of several-subject datasets with DTI and MPRAGE acquisitions for each subject at two separate time points. We've initiated a study to assess robustness of affine registrations of DTI->MPRAGE and compare to robustness of our BSpline tool. This will also help drive the fixing/optimization of our tool.
We have developed the requirements specification, and created the Wiki project page and NAMIC sandbox. Test images have been identified and transfered from the 6 healthy individuals from the Dartmouth data set. We have implemented an initial rigid registration method, for comparison purposes. We also implemented the deformable registration method. Current efforts are underway to write the requsite test engine and develop the evaluation strategy.
Key Investigators
- MGH Algorithms: Dave Tuch, Denis Jen, Josh Snyder
- Kitware Engineering: Luis Ibanez
- Dartmouth DBP 1: Andrew Saykin
Project Week Results: Jan 2006
