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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