PEAR: PEriodic And fixed Rank separation for fast fMRI

摘要

PurposeIn functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial‐temporal resolution trade‐off and increase statistical robustness through increased degrees‐of‐freedom. High‐quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements.