Difference between revisions of "Projects:RegistrationLibrary:RegLib C06"
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|align="left"|LEGEND<br> | |align="left"|LEGEND<br> | ||
[[Image:Button_red_fixed.jpg|20px|lleft]] this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution<br> | [[Image:Button_red_fixed.jpg|20px|lleft]] this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution<br> | ||
Revision as of 15:55, 23 February 2010
Home < Projects:RegistrationLibrary:RegLib C06Back to ARRA main page
Back to Registration main page
Back to Registration Use-case Inventory
Contents
Slicer Registration Use Case Exampe #6: Breast MRI Treatment Assessment
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LEGEND
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 T1 SPGR
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 T1 SPGR
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| 0.9375 x 0.9375 x 1.4 mm 256 x 256 x 112 RAS |
0.9375 x 0.9375 x 1.2 mm 256 x 256 x 130 RAS |
Objective / Background
We seek to align the post-treatment (PostRx) scan with the pre-treatment scan to compare local effects.
Keywords
MRI, breast cancer, intra-subject, treatment assessment, change detection, non-rigid registration
Input Data
reference/fixed : T1 SPGR , 0.9375 x 0.9375 x 1.4 mm voxel size, axial, RAS orientation.
moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal, RAS orientation.
Registration Results
Download
Link to User Guide: How to Load/Save Registration Parameter Presets
Discussion: Registration Challenges
- soft tissue deformations during image acquisition cause large differences in appearance
- contrast enhancement and pathology and treatment changes cause additional differences in image content
- the surface coils used cause strong differences in intensity inhomogeneity.
- resolution and FOV change between the two scans
Discussion: Key Strategies
- because of the strong changes in shape and position, we break the problem down and register each breast separately.
- we perform a bias-field correction on both images before registration
- we use the Multires version of RegisterImages for an initial affine alignment
- the nonlinear portion is then addressed with a BSpline or DiffeomorphicDemons algorithm
- because accuracy is more important than speed here, we increase the sampling rate (i.e. the number of points sampled for the BSpline registration)
