Projects:RegistrationLibrary:RegLib C04

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v3.6.1 Slicer Registration Library Case 04:
Multi-contrast brain MRI of Multiple Sclerosis

Input

exam 1: PD	exam 1: T2		exam 1: T1-Gd
exam 2: PD	exam 2: T2		exam 2: T1-Gd

Modules

• Slicer 3.6.1 recommended modules: BrainsFit, Robust Multiresolution Affine

Objective / Background

This scenario occurs in many forms whenever we wish to assess change in a series of multi-contrast MRI. The follow-up scan(s) are to be aligned with the baseline, but also the different series within each exam need to be co-registered, since the subject may have moved between acquisitions. Hence we have a set of nested registrations. This particular exam features a dual echo scan (PD/T2), where the two structural scans are aligned by default. The post-contrast T1-GdDTPA scan however is not necessarily aligned with the dual echo. Also the post-contrast scan is taken with a clipped field of view (FOV) and a lower axial resolution, with 4mm slices and a 1mm gap (which we treat here as a de facto 5mm slice).

Download

• Registration Library Case 04: MS Multi-contrast series (PD,T2, T1-GdDTPA): Lesion change assessment (Data,Presets, Solution, zip file 17 MB)
• Registration Presets (BrainsFit) (mrml file ,12kB)

Link to User Guide: How to Load/Save Registration Parameter Presets

Keywords

MRI, brain, head, intra-subject, multiple sclerosis, MS, multi-contrast, change assessment, dual echo, nested registration

Input Data

• reference/fixed : PD.1 baseline exam , 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition, RAS orientation.
• fixed T2.1 baseline exam , 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition, RAS orientation. -> (aligned with PD.1, not used for registering)
• moving: T1.1 (GdDTPA contrast-enhanced scan) baseline exam 0.9375 x 0.9375 x 5 mm voxel size, axial acquisition.
• moving: PD.2 follow-up exam 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition.
• moving: T2.2 follow-up exam 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition. -> same orientation as PD2, will have same transform applied
• moving:T1.2-GdDTPA follow-up exam0.9375 x 0.9375 x 5 mm voxel size, axial acquisition. -> undergoes 2 transforms: first to PD.2, then to PD.1

Registration Challenges

• we have multiple nested transforms: each exam is co-registered within itself, and then the exams are aligned to eachother
• potential pathology change can affect the registration
• anisotropic voxel size causes difficulty in rotational alignment
• clipped FOV and low tissue contrast of the post-contrast scan

Key Strategies

• we first register the post-contrast scans within each exam to the PD
• second we register the follow-up PD scan to the baseline PD
• we also move the T2 exam within the same Xform
• we then nest the first alignment within the second
• because of the contrast differences and anisotropic resolution we use Mutual Information as cost function for better robustness

Procedure

1. Load example dataset via OpenScene...
2. Go to the Data module. You should see 6 images (e1_PD, e1_T2 etc.) and 3 solution transforms (Xform_...)
3. Set background view to e1_PD and foreground to e2_PD. Toggle to see misalignment
4. Align Exam 1:
   1. Open Registration / BRAINSFit module
   2. To set all parameters from presets, from the ParameterSet menu, select Gd1-PD1, else choose settings below:
   3. Fixed Image: e1_PD, moving image: e1_T1
   4. Registration Phases: select Include Rigid and Include Affine
   5. Output Settings: under SlicerLinear Transform, select "Create New Linear Transform, then select Rename" and rename it to MyXform_Gd1-PD1
   6. Registration Parameters: change the Number of Samples field to 200,000
   7. Leave all other settings at defaults
   8. Click Apply. Registration should complete within ~ 10 seconds
   9. Go back to the Data module: you should see the e1_T1Gd image moved under the newly created transform
   10. Select e1_T1Gd as new foreground, toggle to see alignment
5. Align Exam 2:
   1. repeat the above steps for e2_PD as fixed and e2_T1Gd as moving
   2. To set all parameters from presets, from the ParameterSet menu, select Gd2-PD2
6. Align Exam 2 with Exam 1:
   1. Open Registration / BRAINSFit module
   2. To set all parameters from presets, from the ParameterSet menu, select PD2-PD1, else choose settings below:
   3. Fixed Image: e1_PD, moving image: e2_PD
   4. Registration Phases: select Include Rigid and Include Affine
   5. Output Settings: under SlicerLinear Transform, select "Create New Linear Transform, then select Rename" and rename it to MyXform_PD2-PD1
   6. Registration Parameters: change the Number of Samples field to 200,000
   7. Leave all other settings at defaults
   8. Click Apply. Registration should complete within ~ 10 seconds
   9. Go back to the Data module: you should see the e2_PD image moved under the newly created transform
   10. Select e2_PD as new foreground, toggle to see alignment
7. Combine Transforms:
   1. move e2_T2 inside the PD2-PD1 transform (same level as e2_PD)
   2. move XForm_Gd2-PD2 and the image inside (e2_T1Gd) inside the PD2-PD1 transform
   3. see image below for how the Data Tree should look after nesting the transforms
   4. Select pairings as fore- and background and click toggle button to check alignment
   5. inparticular see if e2_T1Gd is aligned with e1_T1Gd
8. Harden/Export results:'
   1. Select each registered image in turn, and from right-click menu select Harden Transform. Then immediately rename the node (via MRML Node Inspector below the MRLML tree tab) to distinguish from the original
   2. Select the Xform_Gd2-PD2 when inside the XForm-PD2-PD1 and also select 'Harden Transform. Then immediately rename to Xform_Gd2-PD1.
   3. Choose File/Save to save results.

before registration: after registration:

Registration Results

Unregistered baseline data: PD vs. T1Gd
Unregistered followup data: PD exam 2 vs. exam 1
Registered baseline data
Registered followup data
Lesion change visualization in 3D
Lesion change via subtraction imaging of co-registered PD