Keywords: Spectroscopy, Data Processing, MRSI nuisance signal removal
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.
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