Fan Lam1, 2, Bo Zhao1, 2, Yinan Liu3, Zhi-Pei Liang1, 2, Michael Weiner, 34, Norbert Schuff3, 4
1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; 2Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States; 3Center for Imaging of Neurodegenerative Diseases, Department of Veteran Affairs Medical Center, San Francisco, CA, United States; 4Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
We present a new method for image reconstruction from undersampled data for accelerating fMRI data acquisition. The proposed method integrates a low-rank model of the fMRI image series and a sparsity constraint in a unified mathematical formulation, enabling high quality reconstruction of fMRI images from highly undersampled data. Representative results from simulations based on experimental data were used to demonstrate the performance of the proposed method.