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.
