Projects:RegistrationLibrary:RegLib C06b

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v3.6.1 Registration Library Case #6B: RSNA 2011 DEMO Breast MRI Treatment Assessment

this is the fixed reference image: PreRx Breast MRI with large tumor mass		this is the moving image, to be registered with the reference above: PostRx Breast MRI with tumor largely absent
fixed image/target pre Rx MRI		moving image post Rx MRI

Modules

• Slicer 3.6.1 recommended modules:
  • Modules:BRAINSFit
  • 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_C06B_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

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: affine alignment

1. Go to the BRAINSfit module
   1. select Presets "Xf1_Affine" or set the parameters as given below:
   2. fixed image: "PreRx_left", moving image: "PostRx_left"
   3. Initialize with previous transform: select "Off"
   4. Initialize Transform Mode: check box for use MomentsAlign
   5. Registration Phases: check boxes for Include Rigid ..." and Include Affine registration phase
   6. Output: under Slicer Linear Transform, select new and rename to "Xf1_Affine" or similar
   7. Registration Parameters: this first phase is for initial alignment, we optimize/push for speed
      1. reduce "Number of Iterations" to 200
      2. reduce "Number of Samples" to 20,000
   8. leave rest at defaults
   9. Click Apply. Execution time ~ 10 seconds

• Phase 2: BSpline alignment

1. Go to the BRAINSfit module
   1. select Presets "Xf2_BSpline1" or set the parameters as given below:
   2. fixed image: "PreRx_left", moving image: "PostRx_left"
   3. Initialize with previous transform: select "Xf1_Affine" from phase 1 above
   4. Initialize Transform Mode: check box for Off
   5. only check box for Include BSpline registration phase" , all other boxes off.
   6. Registration Parameters: set "Number of Samples" to 200,000 at least
   7. Output:
      1. Slicer BSpline Transform, select new and rename to "Xf2_BSpline" or similar
      2. Output Image Volume: select new and rename to "PostRx_left_Xf2" or similar
      3. Output Image Pixel Type: check box for "ushort"
   8. Registration Parameters:
      1. set "Number of Samples" to 100,000
      2. set Number of Grid Subdivisions to 7,7,5
      3. set Maximum B-Spline Displacement to 10 [mm]
   9. Click Apply. Execution time ~ 30 seconds

1. 1. Masking: check ROI box, Input Fixed Mask: "58_MRI-label", Input Moving Mask: "64_MRI-label" generated in Phase 3 above.

• Phase 3: Masking

1. Before allowing nonrigid deformations we must mask the aliasing present:
   1. Go to the Editor module.
   2. Select 58_MRI as Master Volume, choose the threshold tool and threshold at intensity ~68 to separate from the background.
   3. Select the Brush tool and cut connections between the aliasing portions and the rest of the image
   4. Select Island Tool and remove aliasing portions
   5. Select Dilate tool to fill holes
   6. Save. Repeat for the 64_MRI volume

• Phase 4: BSpline alignment

1. Go to the BRAINSfit module
   1. Initialize with previous transform: select "Xf2_Affine" from phase 2 above
   2. Output: under Slicer BSpline Transform, select new and rename to "Xf3_BSpline" or similar
   3. Output Image Volume: select new and rename to "64_MRI_Xf3" or similar
   4. Registration Parameters: set "Number of Samples" to 300,000 at least
   5. Number of Grid Subdivisions: 7,7,5
   6. Maximum B-Spline Displacement: set to 5 [mm]
   7. Masking: check ROI box, Input Fixed Mask: "58_MRI-label", Input Moving Mask: "64_MRI-label" generated in Phase 3 above.
   8. Click Apply
2. Note to compare the different registrations: to see the Rigid or Affine registrations, drag the 64_MRI volume inside/onto the respective transform node. To see the BSpline registrations, select the "64_MRI_Xf3" volume as fore- or background.

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

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)