Projects:RegistrationLibrary:RegLib C06

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updated for v4.1 Registration Library Case #6: Breast MRI Treatment Assessment

fixed image/target pre Rx MRI		moving image post Rx MRI

Versions

For the Slicer 3.6 version of this tutorial see here

Modules

• Slicer 3.6.1 recommended modules:
  • Robust Multiresolution Affine
  • Fast Nonrigid BSpline

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:
  • RegLib_C06_BreastCancer_Data.zip (Data,Presets, Solution, zip file 85 MB)
• Presets
  • Registration parameter presets file (.mrml file 20 kB)
  • Link to User Guide: How to Load/Save Registration Parameter Presets
• Documentation
  • download guided tutorial (PowerPoint, 2.6 MB)
  • download guided tutorial (PDF, 1 MB)

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

• Phase 1: Extract left breast image of PreRx and PostRx scan

1. Open the Crop Volume module
   1. 1. Input volume: "PreRx"
      2. Input ROI: "Create new annotation ROI"
      3. ROI visibilité: turn on
      4. Isotropic output voxel: yes (checkbox)
      5. Interpolator: "WindowedSinc" (radio button)
      6. place/drag the color markers visible in the slice views to enclose the left breast only (right side of image)
   2. click on Crop! button
2. go to the Data module
   1. several new nodes were created: look for the "PreRx_subvolume-scale_1" entry, double click and rename to "PreRx_Left"
3. repeat the same for the "PostRx" image
4. Save intermediate results.

• Phase 2: MRI Bias field inhomogeneity correction

1. Open the N4ITKBiasFieldCorrection module
   1. Input Image: PreRx_left
   2. Mask Image: none
   3. Output Volume: create & rename new: "PreRx_left_n4"
   4. leave all parameters at defaults
   5. Apply.
2. repeat for the Post_Rx image.
3. save intermediate results

• Phase 3: Build masks

1. open the Foregroud masking (BRAINS) module (under Segmentation:Specialized)
   1. Input Image Volume: PreRx_left
   2. Output Mask: create & rename new: "PreRx_mask"
   3. Output Image clipped by ROI: none
   4. Configuration Parameters:
      1. ROI Auto Dilate Size: 0.1
      2. defaults for the rest
   5. Apply
2. repeat for Post_Rx_left_n4
3. save intermediate results

• Phase 4: Affine Registration

1. open the General Registration (BRAINS) module

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)