Inter-slice Motion Correction for fMRI

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Home < Inter-slice Motion Correction for fMRI

Objective

   * To perform inter-slice motion correction in fMR images using affine and non-rigid registration methods. 


Progress

   * Implemented and tested b-Spline/affine registration with Mattes-MI and KL metrics. 
   * Coding completed. However, results are far from satisfactory.
   * Progress (maybe) stymied by "poor" resolution of data.
   * Progress (maybe) stymied by "not-so-viable" metrics (MI, etc.).

Algorithms Used From ITK

Metrics

  1. Mean Squared - smooth, and for rigid registration, the minimum is fairly correct (physically)
  2. MI (Wells, Mattes) - very non-smooth - lot of discontinuities. For all motions, results are wrong (possibly because cannot identify a good minimum pt)
  3. KL-Divergence (Chung, Wells, et. al.) - again very non-smooth. Cannot identify a good minimum point.

Transformations

  1. Rigid - works well for ms metric
  2. Affine - does not work for any metric
  3. B-spline non-rigid - tested with ms and kl. Extremely poor results.

Results

Original fMRI

Firdaus image 004.png

MS Metric with Rigid Motion

Original.jpg

MS Metric with Affine Transform

Affine-rms-registered.jpg

Firdaus affine rms.png

Mattes MI metric with rigid transform

Mattes MI metric with affine transform

Firdaus affine mi.png


Issues

  • As can be seen any registration result looks more jagged (along the sagittal and coronal sections).
  • Low sampling rate and image noise - all the metrics are extremely non-smooth as a result.
  • Even the slightest change in initialization results in a large deviation in the registration result.

Open questions

   * Intensity normalization / histogram equalization - would these have any impact on inter-slice registration? Would they affect the computation of the joint-entropies for MI/KL metrics.
   * Should there be some regularization factor on the registration to limit the motion ?

To Do

   * Right now, registration is done on a slice by slice basis. However, due to the low resolution of fMRI and the absence of gradients result in low registration accuracy. We are investigating using alternative image-to-image metrics like the KL divergence. We are also looking at simultaneously registering multiple slices.

Key Investigators

   * Firdaus Janoos, Raghu Machiraju, Steve Pieper, Wendy Plesniak.

Links