A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression

Motivation: Unsuppressed water and lipid signals are several orders of magnitude stronger than the metabolite signals in MRSI, imposing significant challenges in MRSI data processing and image reconstruction. Goal(s): To develop a novel deep learning-based method for nuisance signal removal from MRSI data acquired without suppression. Approach: A neural network with a U-net structure was designed to remove nuisance signals in MRSI, where the input of the network was the Hankel matrix formed by the time-domain MRSI signal. Results: The proposed method was validated using in vivo MRSI data, showing superior performance over the conventional method. Impact: A deep learning-based method is proposed for nuisance signal removal in MRSI. It could enable MRSI without water or lipid suppression with robust performance in practical settings.