Difference between revisions of "Documentation:Nightly:Registration:RegistrationLibrary:RegLib C03"

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==Objective / Background ==
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== Description ==
Goal is to align the DTI image with the structural reference T2 scan that provides accuracte anatomical reference.<br>
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This is a simple case of diffusion MRI. Goal is to align the Diffusion Tensor Image (DTI) with the structural reference T2 scan that provides accuracte anatomical reference.<br>
'''Approach''': we compute the registration transform using a scalar DTI_baseline scan and then apply this transform to the DTI tensor image. Because reorienting tensor data requires a different form of resampling, we must use a dedicated module for this purpose.
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'''Approach''': we compute the registration transform using a scalar DTI_baseline scan and then apply this transform to the DTI tensor image. Because reorienting tensor data requires a different form of resampling, we must use a dedicated module for this purpose. Because DTI acquisitions tend to contain strong geometric distortions, we apply a nonrigid transform to the DTI, which makes resampling necessary.
  
 
== Modules used ==
 
== Modules used ==
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== Procedure ==
 
== Procedure ==
This assumes you have the following: 1) a T2 reference image, 2) a DTI baseline image and  3) the DTI volume (both obtained from the  [http://www.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorEstimation Diffusion Tensor Estimation module]). If you do not have a baseline image, generate a scalar Trace image from the DTI, using the ''Diffusion Tensor Scalar Measurements'' module:
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This assumes you have the following: 1) a T2 reference image, 2) a DTI baseline image and  3) the DTI volume (both obtained from the  [[Documentation/4.1/Modules/DiffusionTensorEstimation|Diffusion Tensor Estimation module]]). If you do not have a baseline image, generate a scalar Trace image from the DTI, using the ''Diffusion Tensor Scalar Measurements'' module:
 
#'''Compute Registration''': open the [[Documentation/Nightly/Modules/BRAINSFit|''General Registration (BRAINS)'' module]]
 
#'''Compute Registration''': open the [[Documentation/Nightly/Modules/BRAINSFit|''General Registration (BRAINS)'' module]]
 
##''Input Images: Fixed Image Volume'': T2
 
##''Input Images: Fixed Image Volume'': T2
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##''Registration Phases'':  select/check ''Rigid'' , ''Rigid+Scale'', ''Affine'', ''BSpline''
 
##''Registration Phases'':  select/check ''Rigid'' , ''Rigid+Scale'', ''Affine'', ''BSpline''
 
##''Main Parameters'':  
 
##''Main Parameters'':  
###increase ''Number Of Samples'' to 300,000
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###increase ''Percentage of Samples'' to 0.006
 
###set  ''B-Spline Grid Size'' to 7,7,5 (we have lower resolution in the IS-direction (z), hence we set a smaller (5) grid size there)
 
###set  ''B-Spline Grid Size'' to 7,7,5 (we have lower resolution in the IS-direction (z), hence we set a smaller (5) grid size there)
 
##Leave all other settings at default
 
##Leave all other settings at default

Latest revision as of 02:21, 27 November 2019

Home < Documentation:Nightly:Registration:RegistrationLibrary:RegLib C03

Back to Registration Library

Slicer Registration Library Case #3: Diffusion Weighted Image Volume: align with structural reference MRI

Input

this is the fixed T2 reference image. All images are aligned into this space
RegArrow NonRigid.png
this is the DTI Baseline scan, to be registered with the T2
this is the DTI tensor image, in the same orientation as the DTI Baseline
fixed image 1/target
T2
moving image 2a
DTI baseline
moving image 2b
DTI tensor

Description

This is a simple case of diffusion MRI. Goal is to align the Diffusion Tensor Image (DTI) with the structural reference T2 scan that provides accuracte anatomical reference.
Approach: we compute the registration transform using a scalar DTI_baseline scan and then apply this transform to the DTI tensor image. Because reorienting tensor data requires a different form of resampling, we must use a dedicated module for this purpose. Because DTI acquisitions tend to contain strong geometric distortions, we apply a nonrigid transform to the DTI, which makes resampling necessary.

Modules used

Alternate Versions

Download (from NAMIC MIDAS)

Why 2 sets of files? The "input data" mrb includes only the unregistered data to try the method yourself from start to finish. The full dataset includes intermediate files and results (transforms, resampled images etc.). If you use the full dataset we recommend to choose different names for the images/results you create yourself to distinguish the old data from the new one you generated yourself.

Keywords

MRI, brain, head, intra-subject, DTI, DWI

Video Screencasts

  1. Movie/screencast showing registration+resampling for Case #03

Procedure

This assumes you have the following: 1) a T2 reference image, 2) a DTI baseline image and 3) the DTI volume (both obtained from the Diffusion Tensor Estimation module). If you do not have a baseline image, generate a scalar Trace image from the DTI, using the Diffusion Tensor Scalar Measurements module:

  1. Compute Registration: open the General Registration (BRAINS) module
    1. Input Images: Fixed Image Volume: T2
    2. Input Images: Moving Image Volume: DTI_baseline
    3. Output Settings:
      1. Slicer BSpline Transform (create new transform, rename to: "Xf1_DTbase-T2_BSpline")
      2. Slicer Linear Transform none
      3. Output Image Volume (create new volume, rename to: "DTIbaseline_Xf1"
    4. Registration Phases: select/check Rigid , Rigid+Scale, Affine, BSpline
    5. Main Parameters:
      1. increase Percentage of Samples to 0.006
      2. set B-Spline Grid Size to 7,7,5 (we have lower resolution in the IS-direction (z), hence we set a smaller (5) grid size there)
    6. Leave all other settings at default
    7. click: Apply; runtime < 1 min.
  2. Resample DTI: We have now computed the registration transform, but the output volume produced above is a registered version of the baseline, which we need for validation only. To get the actual DTI registered we now apply this transform to the tensor image.
    1. Open the Resample DTI Volume module (found under: All Modules) (Do not resample a tensor image with other modules, this one must be used to correctly transform the tensor data)
      1. Input Volume: select DTI
      2. Output Volume: select create new Diffusion Tensor Volume,and rename it to DTI_Xf1
      3. Reference Volume: select T2
      4. Transform Parameters: select transform node "Xf1_DTI-T2_BSpline", for Deformation Field: none ; check the displacement checkbox
      5. Leave all other settings at defaults
      6. Click Apply; runtime ~ 2 min.
    2. set T2 as background and new DTI_Xf1 volume as foreground
    3. fade between back- and foreground to see DTI overlay onto the T2 image. Note that you can also fade via holding the OPTION+CMD keys (mac) + dragging left mouse.

Registration Results

RegLib C03 baseline unregistered.gif baseline & T2 before registration (click to enlarge)
RegLib C03 baseline registered.gif baseline to T2 after affine+nonrigid alignment (click to enlarge)
RegLib C03 DTI registered.gif DTI and T2 before & after registration (click to enlarge)

Discussion: Key Strategies

  • the strong EPI-based distortions of the DTI image make nonrigid registration necessary
  • initial alignment & overlap is sufficient so that no "initialization" methods are necessary and registration can succeed without.
  • contrast & initial pose are similar enough for registration to succeed without any masking. However the DTI estimation procedure does provide an optional mask that is usually very helpful in registering cases with more "distracting" image content.
  • the DTI in this example is isotropic and hence can be resampled directly. If the DTI contains strong anisotropy of ratios 1:3 or greater, reorienting the DTI can lead to strong artifacts (e.g. in axial direction appear as blue cast in the color orientation view). In that case it is necessary to resample the DWI in the original orientation to an isotropic size before reorienting. It may also be advisable to first reorient the DWI and perform the DTI estimation afterwards.

Acknowledgments