Difference between revisions of "Projects:RegistrationLibrary:RegLib C06B"
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=== Modules === | === Modules === |
Revision as of 20:19, 3 June 2012
Home < Projects:RegistrationLibrary:RegLib C06BBack to ARRA main page
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Back to Registration Use-case Inventory
Contents
v3.6.1
Registration Library Case #6: Breast MRI Treatment Assessment
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fixed image/target pre Rx MRI |
moving image post Rx MRI |
Versions
For the Slicer 4.1 version of this tutorial see here
Modules
- Slicer 3.6.1 recommended modules:
Objective / Background
We seek to align the post-treatment (PostRx) scan with the pre-treatment scan to compare local effects (left side only).
Keywords
MRI, breast cancer, intra-subject, treatment assessment, change detection, non-rigid registration
Download
- Data:
- Presets
- Documentation
Input Data
- reference/fixed : 0.44 x 0.44 x 5 mm , 784 x 784 x 30
- moving: 0.68 x 0.68 x 1.5 mm, 515 x 515 x 93
Methods
- Extract left breast image of PreRx scan (ExtractSubvolumeROI module)
- Extract left breast image of PostRx scan (ExtractSubvolumeROI module)
- run MRI Bias field inhomogeneity correction on PreRx scan (MRI Bias Field Correction module)
- run affine registration (Robust Multiresolution Affine module)
- Fixed Image: PreRx_left_BiasCorr
- Moving Image: PostRx_left
- Resample Image: none
- Output transform: Create new linear transform, rename to: Xform_Aff0_MRes
- Fixed Image Mask: none
- Step Size (voxels):5
- Evaluate quality of Affine registration: drag PostRx_left inside the abovecreated Xform node (Data module)
- run Bspline non-rigid registration (Fast Deformable BSpline registration module)
- Iterations: 50
- Grid Size: 5
- Histogram Bins: 100
- Spatial Samples: 80000
- Constrain Deformation: no
- Initial Transform: XForm_Aff0_MRes
- Fixed Image: PreRx_left_BiasCorr
- Moving Image: PostRx_left
- Output Transform: Create New BSpline Transform, rename to: Xform_BSpline1_Aff0Init
- Output Volume: Create New Volume, rename to: PostRx_left_BSpline1
- Apply.
Registration Results
Discussion: Registration Challenges
- soft tissue deformations during image acquisition cause large differences in appearance
- the large tumor recession represents a significant pre/post difference in image content that will influence unmasked intensity-driven registration, which becomes a problem for the non-rigid portion of registration, particularly at higher DOF, because the registration will try to "recreate" the tumor area from the postRx image in order to match the content.
- contrast enhancement and pathology and treatment changes cause additional differences in image content
- the surface coils used cause strong differences in intensity inhomogeneity.
- we have strongly anisotropic voxel sizes with much less through-plane resolution
- 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)