Bryan Clifford1, Fan Lam2, Qiegen Liu2, Chao Ma2, and Zhi-Pei Liang1
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
We present a method which reduces the acquisition time of 3D 1H-MRSI through the integration of parallel imaging and subspace-based imaging. The proposed method enables a better combination of speed, resolution, and SNR than can be provided by parallel imaging or subspace-based imaging alone; however, the removal of nuisance signals from under-sampled MRSI data requires significantly higher reconstruction accuracy than in conventional parallel imaging applications. We solve this problem by incorporating spatial and spectral constraints as well as sensitivity encoding into a recently proposed Union-of-Subspace model. We demonstrate the effectiveness of our method using in vivo data of the brain.
