Projects:RegistrationLibrary:RegLib C06b

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v3.6.3 Registration Library Case #6B: RSNA 2011 DEMO Breast MRI Treatment Assessment

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

Modules

• Slicer 3.6.3 recommended modules: Modules:BRAINSFit

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 ~ 4 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 ~ 60 seconds

Registration Results

unregistered
affine
BSpline 9x9x4 max 15mm
Deformation field for BSpline 9x9x4 max 10mm
BSpline 7x7x5 max 10mm

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After N4 bias correction and histogram equalization:
unregistered
affine
BSpline 7x7x5 max 10mm
Deformation of Post image from unmasked BSpline 7x7x5 max 10mm

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BSpline 7x7x5 max 10mm + tumor mask
Deformation of Post image from masked BSpline 7x7x5 max 10mm

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Demons: defaults
Demons: cost fn: Demons
Demons: cost fn: FastSymm
Demons: cost fn: LogDemons
Demons: cost fn: SymmetricLogDemons
Demons: cost fn: SymmetricLogDemons_Masked

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)