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Abstract #2620

Accelerated fMRI Using Low-Rank Model and Sparsity Constraints

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.

Keywords

accelerate accelerated accelerating acceleration accordingly accurate acquisition activation additive affairs applied assumed benefits biomedical block certain champaign channel choose coil combines complex components computer conducted consider constraint constraints contains context continuation decouple defined denoted denotes design designed detail detected determine determined developed diseases domain efficient efforts electrical encoding encodings engineering estimation evaluated experimental exploiting expressed fast finger form formulation frames fully function grant gray half head highly illustrated illustration joint made maps match matrices matrix medical model modeling navigator near noise norm observation offers operator opportunity original overlaid paradigm parallel partial partially penalty performance periodic problem produced promotes proper propose proposed quadratic radiology rank reconstruct reconstructed reconstruction reconstructions recovery referred repeatedly represent representation representative resemble retrospective reviewed rewritten rigorously sampled sampling scale scanner scanning scheme selected sequential series simulate simulations singular slices solved space sparse sparsely sparsity spatial specifically squares stacked structures studies tapping task transform treated typical vectors veteran white