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Abstract #2356

Achieving High Spatiotemporal Resolution for 1H-MRSI of the Brain

Fan Lam1, Chao Ma1, Qiegen Liu1, Bryan Clifford1,2, and Zhi-Pei Liang1,2

1Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

We present a novel strategy to achieve high spatiotemporal resolution for 1H-MRSI of the brain. The proposed acquisition scheme is characterized by: (a) the use of EPSI-based rapid spatiospectral encoding with an extended k-space coverage; (b) sparse sampling of (k,t)-space; (c) time-interleaved k-space undersampling, and (d) acquisition and use of navigator signals for determining subspace structures. This special acquisition is enabled by a subspace-based data processing and reconstruction method that can effectively remove nuisance signals and obtain high-quality reconstructions from sparse and noisy data. Experimental data have been acquired to demonstrate the potential of the proposed method in producing time-resolved spatiospectral distributions.

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