Chao Ma1, Fan Lam1, Qiang Ning1,2, Bryan A. Clifford1,2, Qiegen Liu1, Curtis L. Johnson1, and Zhi-Pei Liang1,2
1Beckman Institute, University of Illinois Urbana-Champaign, Urbana, IL, United States, 2Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States
Dynamic MRSI measures the temporal changes of metabolite concentrations by acquiring a time series of MRSI data. These data can be used in a range of applications, including the study of the response of a metabolic system to a perturbation. However, high-resolution dynamic MRSI is challenging due to poor SNR resulting from the low concentrations of metabolites. This work presents a new method for high-resolution dynamic 31P-MRSI using high-order partially separable functions. The method has been validated using in vivo dynamic 31P-MRSI experiments, producing encouraging results.
