Difference between revisions of "Projects:RegistrationLibrary:RegLib C03"

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===Objective / Background ===
 
===Objective / Background ===
This is a typical example of DTI processing. Goal is to align the DTI image with a structural scan that provides accuracte anatomical reference. The DTI contains acquisition-related distortion and insufficient contrast to discern anatomical detail.
+
While this example shares a typical objective of DTI processing, it covers additional issues of a strong initial rotation and strong voxel-anisotropy for the raw DWI image acquired. Goal is to align the DTI image with the structural reference T2 scan that provides accuracte anatomical reference. The DTI contains acquisition-related distortion and insufficient contrast to discern anatomical detail.
  
 
=== Keywords ===
 
=== Keywords ===
Line 137: Line 137:
 
*the two images have identical contrast, hence we could consider "sharper" cost functions, such as NormCorr or MeanSqrd. But because of the strong distortions and lower resolution of the moving image, Mutual Information is recommended as the most robust metric.
 
*the two images have identical contrast, hence we could consider "sharper" cost functions, such as NormCorr or MeanSqrd. But because of the strong distortions and lower resolution of the moving image, Mutual Information is recommended as the most robust metric.
 
*often anatomical labels are available from the reference scan. It would be less work to align the anatomical reference with the DTI, since that would circumvent having to resample the complex tensor data into a new orientation. However the strong distortions are better addressed by registering the other direction, i.e. move the DTI into the anatomical reference space.
 
*often anatomical labels are available from the reference scan. It would be less work to align the anatomical reference with the DTI, since that would circumvent having to resample the complex tensor data into a new orientation. However the strong distortions are better addressed by registering the other direction, i.e. move the DTI into the anatomical reference space.
*because we seek to assess/quantify regional size change, we must use a rigid (6DOF) scheme, i.e. we must exclude scaling.
+
*in this example the initial alignment of the two scans is very poor. The strongly oblique orientation of the DWI makes an initial manual alignment step necessary. This step should occur '''before''' converting to the DTI to avoid interpolation artifacts.
*masking is likely necessary to obtain good results.
 
*in this example the initial alignment of the two scans is very poor. The strongly oblique orientation of the DTI makes an initial manual alignment step necessary.
 
*these two images are not too far apart initially, so we reduce the default of expected translational misalignment
 
*because speed is not that critical, we increase the sampling rate from the default 2% to 15%.
 
*we also expect larger differences in scale & distortion than with regular structural scane: so we significantly  (2x-3x) increase the expected values for scale and skew from the defaults.
 
*a good affine alignment is important before proceeding to non-rigid alignment to further correct for distortions.
 
  
 
=== Acknowledgments ===
 
=== Acknowledgments ===

Revision as of 21:18, 17 September 2010

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Slicer Registration Library Exampe #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 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
T2
moving image 2a
DTI baseline
moving image 2b
DTI tensor

Modules


Objective / Background

While this example shares a typical objective of DTI processing, it covers additional issues of a strong initial rotation and strong voxel-anisotropy for the raw DWI image acquired. Goal is to align the DTI image with the structural reference T2 scan that provides accuracte anatomical reference. The DTI contains acquisition-related distortion and insufficient contrast to discern anatomical detail.

Keywords

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

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

Input Data

  • Button red fixed white.jpgreference/fixed : T2w axial, 0.4mm resolution in plane, 3mm slices
  • Button green moving white.jpg moving: Baseline image of acquired DTI volume, corresponds to T2w MRI , 0.9375 x 0.9375 x 1.4 mm voxel size, oblique
  • Button blue tag white.jpg tag: Tensor data of DTI volume, oblique, same orientation as Baseline image. The result Xform will be applied to this volume. The original DWI has 26 directions, the extracted DTI volume has 9 scalars, i.e. 256 x 256 x 36 x 9

Procedures

  • Phase I: LOAD DATA
  1. download example dataset
  2. load into 3DSlicer 3.6.1 (Load Scene)
  • Phase I:REGISTER DTI_base to T2
  1. open Registration : BrainsFit module (presets: Xf1_DTI-T2_unmasked)
    1. Registration Phases:
    2. set T2 as fixed and DTI_base as moving image
      1. select/check Initialize GeometryCenter Align
      2. select/check Include Rigid registration phase
      3. select/check Include ScaleVersor3D registration phase
      4. select/check Include Affine registration phase
      5. select/check Include BSpline registration phase
    3. Output Settings:
      1. select a new transform "Slicer BSpline Transform", rename to "Xf1_DTI-T2_unmasked"
      2. select a new volume "Output Image Volume, rename to "DT_base_Xf1"
    4. Registration Parameters: increase Number Of Samples to 200,000
    5. Registration Parameters: set Number Of Grid Subdivisions to 5,5,3
    6. Leave all other settings at default
    7. click: Apply; runtime < 1 min.
  • Phase II: Resample DTI_mask (presets: DTI_mask_Xf1)
    • we use the above Xform to produce a mask for the T2.
  1. Open Resample Scalar/Vector/DWI Volume module
    1. Input Volume: DTI_mask; Output volume: create new volume, rename to "DTI_mask_Xf1"
    2. Transform Node: "Xf1_DTI-T1_unmasked"
    3. select/check: output-to-input
    4. Interpolation Type: select: nn
    5. click: Apply
    6. Go to Volumes module, select the new "DTI_mask_Xf1", in the Info tab, check the Labelmap box
  • Phase III:REGISTER DTI TO T2 with masking
  1. open Registration : BrainsFit module (presets: Xf2_DTI-T2_masked)
    1. set T2_Xf1 as fixed and DTI_baseline as moving image
      1. Initialize with CenterOfHead Align
      2. select/check Include ScaleVersor3D registration phase
      3. select/check Include Affine registration phase
      4. select/check Include BSpline registration phase
    2. Output BSpline Transform: create new , rename to "Xf2_DTI-T1_masked"
    3. Output Volume: create new, rename to "DTI_base_Xf2"
    4. Registration Parameters: increase Number Of Samples to 200,000
    5. Registration Parameters: set Number Of Grid Subdivisions to 7,7,5
    6. Control of Mask Processing
      1. select/check: ROI (rightmost box)
      2. Input Fixed Mask: select "DTI_mask_Xf1"
      3. Input Moving Mask: select "DTI_mask"
    7. Leave all other settings at default
    8. click: Apply; runtime < 1 min.
  • Phase VI: Resample DTI
  1. Load the combined transform (Add Data)
  2. Open the Resample DTI Volume module (found under: All Modules)
    1. Input Volume: select DTI
    2. Output Volume: select New DTI Volume, rename to DTI_Xf2
    3. Reference Volume: select T2
    4. Transform Parameters: select transform "Xf2_DTI-T2_masked
    5. check box: output-to-input
    6. Leave all other settings at defaults
    7. Click Apply; runtime < 1 min.
  3. Go to the Volumes module, select the newly produced DTI_Xf2 volume
  4. under the Display tab, select Color Orientation from the Scalar Mode menu
  5. set T1 as background and new DTI_Xf2 volume as foreground
  6. Set fade slider to see DTI overlay onto the T2 image

for more details see the tutorial(s) under Downloads

Registration Results

after affine alignment
baseline to T2 after affine alignment



Discussion: Registration Challenges

  • The DTI contains acquisition-related distortions (commonly EPI acquisitions) that can make automated registration difficult.
  • the two images often have strong differences in voxel sizes and voxel anisotropy. If the orientation of the highest resolution is not the same in both images, finding a good match can be difficult.
  • there may be widespread and extensive pathology (e.g stroke, tumor) that might affect the registration if its contrast is different in the baseline and structural reference scan

Discussion: Key Strategies

  • the two images have identical contrast, hence we could consider "sharper" cost functions, such as NormCorr or MeanSqrd. But because of the strong distortions and lower resolution of the moving image, Mutual Information is recommended as the most robust metric.
  • often anatomical labels are available from the reference scan. It would be less work to align the anatomical reference with the DTI, since that would circumvent having to resample the complex tensor data into a new orientation. However the strong distortions are better addressed by registering the other direction, i.e. move the DTI into the anatomical reference space.
  • in this example the initial alignment of the two scans is very poor. The strongly oblique orientation of the DWI makes an initial manual alignment step necessary. This step should occur before converting to the DTI to avoid interpolation artifacts.

Acknowledgments