Difference between revisions of "Documentation/Nightly/Registration/RegistrationLibrary/RegLib C29"

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=== Keywords ===
 
=== Keywords ===
 
MRI, brain, head, intra-subject, DTI, T1, T2, non-rigid, tumor, surgical planning
 
MRI, brain, head, intra-subject, DTI, T1, T2, non-rigid, tumor, surgical planning
−
 
−
===Input Data===
 
−
*reference/fixed : T1 SPGR , 0.5x0.5x1 mm voxel size, 512 x 512 x 176
 
−
*moving 1: T2 0.5x0.5x1.5 mm voxel size, 512 x 512 x 92
 
−
*moving 2a: DTI  baseline: 1 x 1 x 3 mm, 256 x 256 x 41
 
−
*moving 2b: 1 x 1 x 3 mm, 256 x 256 x 41 x 9 (tensor), original: DWI 256 x 256 x 41 x 32 directions
 
  
 
===Registration Challenges===
 
===Registration Challenges===
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*the DTI baseline is similar in contrast to a T2, albeit at much lower resolution
 
*the DTI baseline is similar in contrast to a T2, albeit at much lower resolution
 
*we have different amounts of voxel-anisotropy
 
*we have different amounts of voxel-anisotropy
−
*a direct registration of the DTI_baseline to the SPGR will fail, hence a 2-step approach is required
 
  
−
===Key Strategies===
+
===Registration Approach ===
−
*'''Slicer 4 recommended modules:  [http://www.slicer.org/slicerWiki/index.php/Modules:BRAINSFit BrainsFit]
+
*a direct registration of the DTI_baseline to the T1 is not recommended, since the T2 is a better match in contrast to the DTI baseline. We therefore first align the DTI with the T2 and then also register the T1 to the T2.
−
*to align the DTI with the T1 we need 2 registration steps: 1.align the T2 with the T1 and 2. align the DTI with the T2. The DTI baseline scan has contrast most similar to the T2 and hence is best registered against the T2. However the structural reference scan with highest resolution and tissue contrast is the T1.
+
*the DTI-T2 registration includes non-rigid deformation to correct for the strong distortions from the EPI acquisition. Because of the nonrigid component a mask of the brain parenchyma helps in obtaining a meaningful transform. Registration without a mask is possible but not as good as with a mask.
−
*we therefore use the following approach: 1) we first co-register the T2 with the SPGR T1.  2) we register the DTI baseline to the registered/resampled T2;  3) resample the DTI volume with the new transform
+
*The DTI estimation provides an automated mask for the DTI_baseline scan, but we have no mask for the T2. We obtain a T2 mask through separate segmentation.  
−
*the DTI-T2 registration includes non-rigid deformation to correct for the strong distortions from the EPI acquisition. Because of the nonrigid component a mask of the brain parenchyma helps greatly in obtaining a meaningful transform.  
 
−
*The DTI estimation provides an automated mask for the DTI_baseline scan, but we have no mask for the T2. We can either obtain one through separate segmentation or by sending the DTI_mask through an additional registration step. In this example we show the latter.
 
 
*thus the full pipeline is this:
 
*thus the full pipeline is this:
−
:#Affine align T2-T1, incl. resampled T2 volume = T2r
+
:#build a mask for the T2.
−
:#Affine+BSpline align of DTI_baseline to T2r (unmasked)
+
::#Affine+BSpline align of DTI_baseline to T2r (unmasked)
 
:#Resample DTI_mask with above BSpline -> mask for the T2r
 
:#Resample DTI_mask with above BSpline -> mask for the T2r
 
:#repeat Affine+BSpline align of DTI_base to T2r, WITH masks
 
:#repeat Affine+BSpline align of DTI_base to T2r, WITH masks
 
:#resample DTI with result Affine+BSpline transform
 
:#resample DTI with result Affine+BSpline transform
 +
:#Affine align T2-T1, incl. resampled T2 volume = T2r
 
=== Procedures ===
 
=== Procedures ===
−
*'''Phase I: Preprocessing: Build DWI mask + baseline'''
+
*'''Phase I: build a T2 mask'''
−
#open the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionWeightedMasking ''Modules:Diffusion:DiffusionWeightedImages:DiffusionWeightedVolumeMasking'' ] module  
+
A selection of 6 different methods to build a mask are shown here. Below is one of those, using the "GrowCut" tool in the Editor
 +
#a video of the steps below is found here.
 +
#Open the editor tool, select the T2 as master volume and create a new label map.
 +
#select the brush tool, set the label to 2 and the brush size to 80 mm or larger (filling almost entire FOV)
 +
#on 4 or 5 slices select the entire head and a good portion of the background
 +
#switch to the ''Level Tracing'' effect/tool. Switch the label to 1.
 +
#On the same slices as before, place the cursor near the brain surface until the yellow outline shows a good enclosure of the brain at that slice. Click the left mouse to fill.
 +
#you should now have a handful of slices, even distributed, where label 1 describes the brain parenchyma and label 2 everything else.
 +
#switch to the ''GrowCutEffect'' and click Apply.
 +
#Apply some Dilation+Erosion filtering to cleanup the result.
 +
 
 +
*'''Phase II: Preprocessing: Build DWI mask + baseline'''
 +
#open the [[Documentation/Nightly/Modules/DiffusionWeightedVolumeMasking ''Modules:Diffusion:DiffusionWeightedImages:DiffusionWeightedVolumeMasking'' ]] module  
 
##''Input DWI Volume'': "DWI"
 
##''Input DWI Volume'': "DWI"
−
##''Output Baseline Volume'':  ''Create New Volume'', rename to "DWI_baseline"
+
##''Output Baseline Volume'':  ''Create & Rename New Volume'', rename to "DWI_baseline"
−
##''Output Threshold Mask'': ''Create New Volume'', rename to "DWI_mask"
+
##''Output Threshold Mask'': ''Create & Rename New Volume'', rename to "DWI_mask"
 
##Leave other settings at default; click ''Apply''
 
##Leave other settings at default; click ''Apply''
 +
##You should obtain a baseline scalar image and a mask label map.
 
*'''Phase II: Preprocessing: Convert DWI -> DTI'''
 
*'''Phase II: Preprocessing: Convert DWI -> DTI'''
−
#open [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorEstimation "Diffusion Tensor Estimation" module] (menu: Diffusion:DiffusionWeightedImages: DiffusionTensorEstimation)
+
#open [[Documentation/Nightly/Modules/DiffusionTensorEstimation "Diffusion Tensor Estimation" module]] (menu: Diffusion:DiffusionWeightedImages: DiffusionTensorEstimation)
 
##''Input DWI Volume'': DWI_iso,  
 
##''Input DWI Volume'': DWI_iso,  
 
##''Output DTI Volume'': create new, rename to "DTI_iso"
 
##''Output DTI Volume'': create new, rename to "DTI_iso"

Revision as of 15:31, 9 May 2013

Home < Documentation < Nightly < Registration < RegistrationLibrary < RegLib C29

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Slicer Registration Library Case #29: Intra-subject Brain DTI

Input

this is the fixed reference image. All images are aligned into this space lleft this is the T2 reference image, serves as target to the DTI baseline, but is itself aligned to the SPGR lleft 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/target
T1 SPGR 		moving image 1
T2 		moving image 2a
DTI baseline 		moving image 2b
DTI tensor

Slicer4 Modules used

Objective / Background

This is a classic case of a multi-sequence MRI exam we wish to spatially align to the anatomical reference scan (T1-SPGR). The scan of interest is the DTI image to be aligned for surgical planning/reference.

Download (from NAMIC MIDAS)

Keywords

MRI, brain, head, intra-subject, DTI, T1, T2, non-rigid, tumor, surgical planning

Registration Challenges

  • The DWI sequence (EPI) contains strong distortions we seek to correct via non-rigid alignment
  • The DWI sequence also contains strong intensity inhomogeneity (coil bias)
  • the DTI baseline is similar in contrast to a T2, albeit at much lower resolution
  • we have different amounts of voxel-anisotropy

Registration Approach

  • a direct registration of the DTI_baseline to the T1 is not recommended, since the T2 is a better match in contrast to the DTI baseline. We therefore first align the DTI with the T2 and then also register the T1 to the T2.
  • the DTI-T2 registration includes non-rigid deformation to correct for the strong distortions from the EPI acquisition. Because of the nonrigid component a mask of the brain parenchyma helps in obtaining a meaningful transform. Registration without a mask is possible but not as good as with a mask.
  • The DTI estimation provides an automated mask for the DTI_baseline scan, but we have no mask for the T2. We obtain a T2 mask through separate segmentation.
  • thus the full pipeline is this:
  1. build a mask for the T2.
  1. Affine+BSpline align of DTI_baseline to T2r (unmasked)
  1. Resample DTI_mask with above BSpline -> mask for the T2r
  2. repeat Affine+BSpline align of DTI_base to T2r, WITH masks
  3. resample DTI with result Affine+BSpline transform
  4. Affine align T2-T1, incl. resampled T2 volume = T2r

Procedures

  • Phase I: build a T2 mask

A selection of 6 different methods to build a mask are shown here. Below is one of those, using the "GrowCut" tool in the Editor

  1. a video of the steps below is found here.
  2. Open the editor tool, select the T2 as master volume and create a new label map.
  3. select the brush tool, set the label to 2 and the brush size to 80 mm or larger (filling almost entire FOV)
  4. on 4 or 5 slices select the entire head and a good portion of the background
  5. switch to the Level Tracing effect/tool. Switch the label to 1.
  6. On the same slices as before, place the cursor near the brain surface until the yellow outline shows a good enclosure of the brain at that slice. Click the left mouse to fill.
  7. you should now have a handful of slices, even distributed, where label 1 describes the brain parenchyma and label 2 everything else.
  8. switch to the GrowCutEffect and click Apply.
  9. Apply some Dilation+Erosion filtering to cleanup the result.
  • Phase II: Preprocessing: Build DWI mask + baseline
  1. open the Documentation/Nightly/Modules/DiffusionWeightedVolumeMasking ''Modules:Diffusion:DiffusionWeightedImages:DiffusionWeightedVolumeMasking'' module
    1. Input DWI Volume: "DWI"
    2. Output Baseline Volume: Create & Rename New Volume, rename to "DWI_baseline"
    3. Output Threshold Mask: Create & Rename New Volume, rename to "DWI_mask"
    4. Leave other settings at default; click Apply
    5. You should obtain a baseline scalar image and a mask label map.
  • Phase II: Preprocessing: Convert DWI -> DTI
  1. open Documentation/Nightly/Modules/DiffusionTensorEstimation "Diffusion Tensor Estimation" module (menu: Diffusion:DiffusionWeightedImages: DiffusionTensorEstimation)
    1. Input DWI Volume: DWI_iso,
    2. Output DTI Volume: create new, rename to "DTI_iso"
    3. Output Baseline Volume: create new, rename to "DWI_iso_baseline"
  2. Click: Apply
  • Phase III: register T2 to T1
  1. open the General Registration (BRAINS) module
    1. Fixed Image Volume: T1
    2. Moving Image Volume: T2
    3. Output Settings:
      1. Slicer BSpline Transform": none
      2. Slicer Linear Transform: create new transform, rename to "Xf1_T2-T1"
      3. Output Image Volume: create new volume, rename to "T2_Xf1"
    4. Registration Phases: check boxes for Rigid and Affine
    5. Main Parameters:
      1. Number Of Samples: 200,000
    6. Leave all other settings at default
    7. click: Apply; runtime < 10 sec (MacPro QuadCore 2.4GHz)
  • Phase IV: Register DTI (masked)
  1. open the General Registration (BRAINS) module
    1. Fixed Image Volume: FLAIR_Xf1
    2. Moving Image Volume: DWI_iso_baseline
    3. Output Settings:
      1. Slicer BSpline Transform": create new transform, rename to "Xf2_DTI-T1"
      2. Slicer Linear Transform: none
      3. Output Image Volume: create new volume, rename to DWI_baseline_Xf2
    4. Regstration Phases: check boxes for Rigid, Affine and BSpline
    5. Main Parameters:
      1. Number Of Samples: 300,000
      2. B-Spline Grid Size: 7,7,5
    6. Mask Option: select ROIAUTO button
      1. (ROIAUTO) Output fixed mask: create new volume
      2. (ROIAUTO) Output moving mask: create new volume
    7. Leave all other settings at default
    8. click: Apply; runtime 1-2 min (MacPro QuadCore 2.4GHz)
  • Phase V: Resample DTI
  1. Open the Resample DTI Volume module (under All Modules menu; note this is distinct from the ResampleScalarVectorDWIVolume used above)
    1. Input Volume: DTI
    2. Output Volume: create new DTI Volume, rename to DTI_Xf2
    3. Reference Volume: T2_Xf1
    4. Transform Node: select "Xf2_DTI-T1 created above
    5. check box: displacement
  2. leave all other settings at defaults
  3. Click Apply; runtime ~ 3 min.
  4. set T1 or FLAIR as background and the new DTI_Xf2 volume as foreground
  5. Move fade slider to see DTI overlay onto the structural image

Registration Results

Registered DTI superimposed on SPGR and T2 registered (cycles show T1 and T2 and color DTI overlay)

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