Projects:RegistrationLibrary:RegLib C06

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Slicer Registration Use Case Exampe #6: Breast MRI Treatment Assessment

			LEGEND this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution this indicates the moving image that determines the registration transform.
axial		T1 SPGR
0.44 x 0.44 x 5 mm 784 x 784 x 30 RAS		0.68 x 0.68 x 1.5 mm 515 x 515 x 93 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.

Methods

1. Extract left breast image of PreRx scan (ExtractSubvolumeROI module)
2. Extract right breast image of PreRx scan (ExtractSubvolumeROI module)
3. run MRI Bias field inhomogeneity correction on PreRx scan (MRI Bias Field Correction module)
4. run affine registration (RegisterImages Multires module)
   1. Fixed Image: PreRx_left_BiasCorr
   2. Moving Image: PostRx_left
   3. Resample Image: none
   4. Output transform: Create new linear transform, rename to: Xform_Aff0_MRes
   5. Fixed Image Mask: none
   6. Step Size (voxels):5
5. Evaluate quality of Affine registration: drag PostRx_left inside the abovecreated Xform node (Data module)
6. run Bspline non-rigid registration (Deformable BSpline registration module)
   1. Iterations: 50
   2. Grid Size: 5
   3. Histogram Bins: 100
   4. Spatial Samples: 80000
   5. Constrain Deformation: no
   6. Initial Transform: XForm_Aff0_MRes
   7. Fixed Image: PreRx_left_BiasCorr
   8. Moving Image: PostRx_left
   9. Output Transform: Create New BSpline Transform, rename to: Xform_BSpline1_Aff0Init
   10. Output Volume: Create New Volume, rename to: PostRx_left_BSpline1
   11. Apply.

Download

• download entire package (Data,Presets,Tutorial, Solution, zip file 9.8 MB)

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.
• 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)