Keywords: Spectroscopy, Machine Learning/Artificial IntelligenceWe introduced a data-driven denoiser trained in a self-supervised fashion as a novel spatial-temporal constraint for MRSI reconstruction. Our proposed denoiser was trained using noisy data only via the Noise2void framework that trains an interpolation network exploiting the statistical differences between spatiotemporally correlated signals and uncorrelated noise. The trained denoiser was then integrated into an iterative MRSI reconstruction formalism as a Plug-and-Play prior. An additional physics-based subspace constraint was also included into the reconstruction. Simulation and in vivo results demonstrated impressive SNR-enhancing reconstruction ability of the proposed method, with improved performance over a state-of-the-art subspace method.
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