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Improved motion correction of fMRI time-series corrupted with major head movement using extended motion-corrected independent component analysis

Publication ,  Journal Article
Liao, R; McKeown, M; Krolik, J
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
December 1, 2005

An extension of previously-described Motion-Corrected Independent Component Analysis (MCICA) for improved correction of significant patient head motion in fMRI data is proposed. For fMRI time-points corrupted with relatively large motion, i.e. on the order of half a voxel, only partial images subject to minimal interpolation artifact are initially used in MCICA, allowing for an accurate estimation of the activation weights of the underlying ICA components. The remaining voxels that are irretrievably corrupted with gross motion in the motion-corrupted time-points are treated as missing data, so the final component maps of the ICA components are estimated from an optimally motionless reference ensemble. Interpolation artifact therefore is minimized in the final registered image, which can be mathematically expressed as a weighted combination of the extended reference ensemble. Experiments demonstrate that the proposed method was robust to the presence of simulated activation and the number of reference images used. While the previous version of MCICA already achieved noticeably decreased registration error than SPM and AIR, the proposed method further reduced the error by thirty percent when correcting simulated gross movements applied on real fMRI time-points. With a real fMRI time-series acquired during a motor-task, further increased mutual information and more clustered activation in the primary and supplementary motor areas were observed. © Springer-Verlag Berlin Heidelberg 2005.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 1, 2005

Volume

3765 LNCS

Start / End Page

346 / 355

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Liao, R., McKeown, M., & Krolik, J. (2005). Improved motion correction of fMRI time-series corrupted with major head movement using extended motion-corrected independent component analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3765 LNCS, 346–355. https://doi.org/10.1007/11569541_35
Liao, R., M. McKeown, and J. Krolik. “Improved motion correction of fMRI time-series corrupted with major head movement using extended motion-corrected independent component analysis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3765 LNCS (December 1, 2005): 346–55. https://doi.org/10.1007/11569541_35.
Liao R, McKeown M, Krolik J. Improved motion correction of fMRI time-series corrupted with major head movement using extended motion-corrected independent component analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005 Dec 1;3765 LNCS:346–55.
Liao, R., et al. “Improved motion correction of fMRI time-series corrupted with major head movement using extended motion-corrected independent component analysis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3765 LNCS, Dec. 2005, pp. 346–55. Scopus, doi:10.1007/11569541_35.
Liao R, McKeown M, Krolik J. Improved motion correction of fMRI time-series corrupted with major head movement using extended motion-corrected independent component analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005 Dec 1;3765 LNCS:346–355.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 1, 2005

Volume

3765 LNCS

Start / End Page

346 / 355

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences