Matthew J. Muckley1, 2, Scott J. Peltier1, 2, Douglas C. Noll1, 2, Jeffrey A. Fessler3
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States; 2Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States; 3Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States
A novel application of low rank methods combined with temporal Fourier sparsity regularization and random sampling for removal of physiological noise in functional MRI is presented. This approach has the potential to recover high temporal frequency characteristics of the physiological noise while sampling these signals well below the Nyquist rate on average. The method is validated in a resting state connectivity task, where it is used to reconstruct a data set with high spatiotemporal resolution before removing physiological noise using low pass filtering.