Projects:RegistrationLibrary:RegLib C02

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Slicer Registration Library Case #02: Intra-subject Brain MR FLAIR to MR T1

this is a passive image to which the calculated transform is applied. It is a label-map in the same space as the moving FLAIR image this is the fixed reference image. All images are aligned into this space lleft this is the moving image. The transform is calculated by matching this to the reference image
mask image fixed image/target moving image
lleft this is a passive image to which the calculated transform is applied. It is a label-map in the same space as the moving FLAIR image
resample segmentation labelmap


Modules

Objective / Background

This scenario occurs in many forms whenever we wish to align all the series from a single MRI exam/session into a common space. Alignment is necessary because the subject likely has moved in between series. As additional files we have a mask for the fixed/reference image and a labelmap for the moving image we need to move along .

Keywords

MRI, brain, head, intra-subject, FLAIR, T1, defacing, masking, labelmap, segmentation

Input Data

  • reference/fixed : T1 SPGR , 1x1x1 mm voxel size, 256 x 256 x 146, sagittal,
  • mask: skull stripping labelmap obtained from above SPGR
  • moving: T2 FLAIR 1.2x1.2x1.2 mm voxel size, sagittal
  • tag: segmentation labelmap obtained from above FLAIR, to be resampled with result transform

Registration Challenges

  • the amount of misalignment is small. Subject did not leave the scanner in between the two acquisitions, but we have some head movement.
  • we know the underlying structure/anatomy did not change, but the two distinct acquisition types may contain different amounts of distortion
  • the T1 high-resolution had a "defacing" applied, i.e. part of the image containing facial features was removed to ensure anonymity. The FLAIR is lower resolution and contrast and did not need this. The sharp edges and missing information in part of the image may cause problems.
  • we have a skull stripping label map of the fixed image (T1) that we can use to mask out the non-brain part of the image and prevent it from actively participating in the registration.
  • we have one or more label-maps attached to the moving image that we also want to align.
  • the different series have different dimensions, voxel size and field of view. Hence the choice of which image to choose as the reference becomes important. The additional image data present in one image but not the other may distract the algorithm and require masking.
  • hi-resolution datasets may have defacing applied to one or both sets, and the defacing-masks may not be available
  • the different series have different contrast. The T1 contains good contrast between white (WM) and gray matter (GM) , and pathology appears as hypointense. The FLAIR on the other hand shows barely any WM/GM contrast and the pathology appears very dominantly as hyperintense.

Key Strategies

  • Slicer 3.6 recommended modules: BrainsFit, Robust Multiresolution Affine
  • we use an affine transform with 12 DOF (rather than a rigid one) to address distortion differences between the two protocols
  • we choose the SPGR as the anatomical reference. Unless there are overriding reasons, always use the highest resolution image as your fixed/reference, to avoid loosing data through the registration.
  • the defacing of the SPGR image introduces sharp edges that can be detrimental. We apply a multiresolution scheme at least. If this fails we mask that area or better still the brain. As a general rulle, if you have the mask available, use it.
  • because of the contrast differences and the defacing we use Mutual Information as the cost function.
  • because of the combined effects of rotational misalignment, defacing, pathology and contrast differences, we use a multi-resolution approach (Register Images MultiRes).

Procedures

BrainsFit

  1. download example dataset
  2. load into 3DSlicer 3.6
  3. open Registration : BrainsFit module
    1. Input Parameters: set SPGR as fixed and FLAIR as moving image
    2. Registration Phases: select "Include Affine registration phase"
    3. Output Settings: select "New Linear Transform" under Output Transform
    4. Control of Mask Processing: select ROI checkbox and under
      1. for Input Fixed Mask select ICC image
      2. for Input Moving Mask select "none"
    5. accept all other defaults & click apply
  4. go to Data module and move FLAIR and labelmap under the result transform
  5. right click on either image and select Harden Transform to apply & resample
  6. save result images/scene

MultiresolutionAffine

  1. download example dataset
  2. load into 3DSlicer 3.6
  3. open Registration : RobustAffineMultiresolution module
    1. set SPGR as fixed and FLAIR as moving image
    2. set affine as desired transform
    3. set mask image to the ICC labelmap (BrainsFit only)
    4. accept all defaults & click apply
  4. go to Data module and move FLAIR and labelmap under the result transform
  5. right click on either image and select Harden Transform to apply & resample
  6. save result images/scene

for more details see the tutorial under Downloads

Registration Results

Unregistered Data + segmentation labelmap Registration Result: FLAIR + segmentation aligned with SPGR

Download

this case is still under active development. Comments and priority requests welcome to the slicer-users mailing list


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