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.).

Results

Affine

• affine motion with all metrics degrades the inter-slice registration.

Non-Rigid

• B-spline deformation with KL metric produces very minimal change (in a least squares sense), while MI produces more changes but doesn't look correct.

Images

Original fMRI

Affine motion with mean square error metric

Affine motion with Mattes MI error metric

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.