Difference between revisions of "Projects:RegistrationLibrary:RegLib C09"

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===Download ===
===Download ===
*'''[[Media:RegLib_09_fMRI_full.zip‎|download entire package  <small> (Data,Presets,Tutorial, Solution, zip file 33.7 MB) </small>]]'''  
*'''[[Media:RegLib_09_fMRI_full.zip‎|download entire package  <small> (Data,Presets,Tutorial, Solution, zip file 39 MB) </small>]]'''  
*Tutorial only
*Tutorial only

Revision as of 18:35, 18 February 2010

Home < Projects:RegistrationLibrary:RegLib C09

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Slicer Registration Library Exampe #9: Functional MRI aligned with structural reference MRI

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 LEGEND

lleft this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution
lleft this indicates the moving image that determines the registration transform.

lleft T1 structural reference lleft fMRI 4D volume
0.5 x 0.5 x 1.0 mm axial
512 x 512 x 176
1.8 x 1.8 x 4 mm
axial oblique
128 x 128 x 19 x 125 timepoints

Objective / Background

This is a typical example of fMRI pre-processing. Goal is to align the fMRI image with a structural scan that provides accuracte anatomical reference. The fMRI contains acquisition-related distortion and low contrast to discern much anatomical detail. We also have pathology (stroke) with variable contrast across different MRI protocols.


MRI, brain, head, intra-subject, fMRI

Input Data

  • Button red fixed white.jpgreference/fixed : T1
  • Button green moving white.jpg moving: fMRI sequence of motor task (right hand clench)

Registration Results

after affine alignment


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

Discussion: Registration Challenges

  • the fMRI contains acquisition-related distortions that can make automated registration difficult.
  • the fMRI contains low tissue contrast, making automated intensity-based 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

  • masking is likely necessary to obtain good results.
  • in this example the initial alignment of the two scans is not excessive.
  • 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.