Abstract #2361

Removal of Nuisance Signal from Sparsely Sampled 1H-MRSI Data Using Physics-based Spectral Bases

Qiang Ning^1,2, Chao Ma², Fan Lam², Bryan Clifford^1,2, and Zhi-Pei Liang^1,2

¹Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, United States, ²Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, Urbana, IL, United States

A novel nuisance removal method is proposed for ¹H-MRSI. The method uses spectral bases generated for water and subcutaneous lipids using quantum simulation, and can perform nuisance signal removal directly from (k,t)-space data. Consequently, the proposed method is able to handle sparsely sampled MRSI data, which provides a desirable flexibility for designing accelerated ¹H-MRSI data acquisition schemes. Experimental results demonstrate that the proposed method is capable of removing nuisance signals from 1H-MRSI data acquired from the brain without water and lipid suppression pulses.

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