Difference between revisions of "Inter-slice Motion Correction for fMRI"
From NAMIC Wiki
| Line 1: | Line 1: | ||
== Objective == | == Objective == | ||
| + | * To perform inter-slice motion correction in fMR images using affine and non-rigid registration methods. | ||
| − | |||
== Progress == | == Progress == | ||
| − | + | * Implemented and tested b-Spline/affine registration with Mattes-MI and KL metrics. | |
| − | * Implemented and tested b-Spline/affine registration with | ||
== Results == | == Results == | ||
=== Affine === | === Affine === | ||
* affine motion with all metrics degrades the inter-slice registration. | * 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. | ||
| + | |||
| + | === Issues === | ||
| + | * 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. | ||
| + | * Any registration result looks more jagged (along the sagittal and coronal sections) | ||
| Line 16: | Line 23: | ||
* Should there be some regularization factor on the registration to limit the motion ? | * Should there be some regularization factor on the registration to limit the motion ? | ||
| − | |||
== To Do == | == To Do == | ||
Revision as of 07:40, 5 September 2007
Home < Inter-slice Motion Correction for fMRIContents
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.
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.
Issues
- 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.
- Any registration result looks more jagged (along the sagittal and coronal sections)
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.
