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Abstract #3486

Locally low-rank denoising of multi-echo fMRI data preserves detection of resting-state networks following scan truncation

Nolan K Meyer1,2, Daehun Kang2, Zaki Ahmed2, Myung-Ho In2, Yunhong Shu2, John Huston III2, Matt A Bernstein2, and Joshua D Trzasko2
1Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States, 2Department of Radiology, Mayo Clinic, Rochester, MN, United States


Functional magnetic resonance imaging (fMRI) has inherent limitations of fast acquisitions due to low signal-to-noise ratio (SNR) and artifacts. Multi-echo fMRI acquires images at multiple TEs, increasing robustness to off-resonance based signal loss and improving sensitivity to neural activity. However, noise is substantial given limitations of EPI, although consistent across TEs contributing to prolonged scan times required for sufficient statistical power. Reduction of acquisition time with reconstruction and processing techniques is of interest. This study extends preliminary work on locally low-rank denoising of multi-echo fMRI data to explore scan time reduction through processing with retrospective truncation.

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