Projects:RegistrationLibrary:RegLib C06B

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v3.6.1 Slicer3-6Announcement-v1.png Registration Library Case #6: Breast MRI Treatment Assessment

this is the fixed reference image. All images are aligned into this space lleft this is the DTI Baseline scan, to be registered with the T2
fixed image/target
pre Rx MRI
moving image
post Rx MRI


For the Slicer 4.1 version of this tutorial see here


Objective / Background

We seek to align the post-treatment (PostRx) scan with the pre-treatment scan to compare local effects (left side only).


MRI, breast cancer, intra-subject, treatment assessment, change detection, non-rigid registration


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


  1. Extract left breast image of PreRx scan (ExtractSubvolumeROI module)
  2. Extract left breast image of PostRx scan (ExtractSubvolumeROI module)
  3. run MRI Bias field inhomogeneity correction on PreRx scan (MRI Bias Field Correction module)
  4. run affine registration (Robust Multiresolution Affine 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 (Fast 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.

Registration Results

unregistered affine registered Bspline registered

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)