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Abstract #3877

Locally low-rank denoising of complex-valued EPI reconstructions preceding task fMRI analysis

Nolan K Meyer1, Norbert G Campeau2, David F Black2, Kirk M Welker2, Jeffrey L Gunter2, Uten Yarach2, Daehun Kang2, MyungHo In2, John Huston III2, Yunhong Shu2, Matt A Bernstein2, and Joshua D Trzasko2
1Biomedical Engineering and Physiology, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States, 2Radiology, Mayo Clinic, Rochester, MN, United States

This work examines the removal of physiologic and measurement noise (i.e. "denoising") of complex-valued EPI timecourse data preceding task-based fMRI analysis. The locally low-rank properties of the EPI data are leveraged with a blockwise singular value thresholding (BSVT) algorithm applied as a preprocessing step. Two EPI datasets (single-band and SMS multi-band) concomitant with task-based finger tapping fMRI exams were preprocessed with BSVT; activation maps were then compared by board-certified neuroradiologists. BSVT denoising of complex-valued fMRI time-course data prior to task analysis improves statistical confidence in activation areas identified by conventional processing or reveals new activation regions under fixed confidence levels.

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