Difference between revisions of "Inter-slice Motion Correction for fMRI"
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* Progress (maybe) stymied by "not-so-viable" metrics (MI, etc.). | * Progress (maybe) stymied by "not-so-viable" metrics (MI, etc.). | ||
− | == | + | == Algorithms Used From ITK == |
+ | |||
+ | === Metrics === | ||
− | + | # Mean Squared - smooth, and for rigid registration, the minimum is fairly correct (physically) | |
+ | # MI (Wells, Mattes) - very non-smooth - lot of discontinuities. For all motions, results are wrong (possibly because cannot identify a good minimum pt) | ||
+ | # KL-Divergence (Chung, Wells,'' et. al.'') - again very non-smooth. Cannot identify a good minimum point. | ||
− | + | === Transformations === | |
− | + | # Rigid - works well for ms metric | |
+ | # Affine - does not work for any metric | ||
+ | # B-spline non-rigid - tested with ms and kl. Extremely poor results. | ||
− | === | + | == Results == |
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− | + | '''Original fMRI ''' | |
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[[Image:firdaus_image_004.png]] | [[Image:firdaus_image_004.png]] | ||
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+ | '''MS Metric with Rigid Motion''' | ||
[[Image:original.jpg]] | [[Image:original.jpg]] | ||
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+ | '''MS Metric with Affine Transform''' | ||
+ | [[Image:firdaus_affine_rms.png]] | ||
− | [[Image: | + | '''Mattes MI metric with affine transform ''' |
− | + | [[Image:firdaus_affine_mi.png]] | |
+ | ''' something ''' | ||
[[Image:affine-rms-registered.jpg]] | [[Image:affine-rms-registered.jpg]] | ||
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=== Issues === | === Issues === |
Revision as of 23:46, 24 October 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. * 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
- Mean Squared - smooth, and for rigid registration, the minimum is fairly correct (physically)
- MI (Wells, Mattes) - very non-smooth - lot of discontinuities. For all motions, results are wrong (possibly because cannot identify a good minimum pt)
- KL-Divergence (Chung, Wells, et. al.) - again very non-smooth. Cannot identify a good minimum point.
Transformations
- Rigid - works well for ms metric
- Affine - does not work for any metric
- B-spline non-rigid - tested with ms and kl. Extremely poor results.
Results
Original fMRI
MS Metric with Rigid Motion
MS Metric with Affine Transform
Mattes MI metric with affine transform
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.