Multidimensional MR Spatiospectral Reconstruction Integrating Subspace Modeling and a Plug&Play Denoiser with Recurrent Features

Motivation: Multidimensional MR spatiospectral imaging (MD-MRSI) has many applications but is challenging due to high dimensionality and limited SNR. Subspace and learning-based methods have both demonstrated success. Goal(s): To develop a new MD-MRSI reconstruction method synergizing subspace modeling and a spatiospectral denoiser that can be ‘pre-learned’ without noisy/clean image pairs. Approach: A self-supervised training strategy was proposed to learn a network-based denoiser combining convolutional, fully-connected, and recurrent features and effectively exploiting multidimensional “correlations”. A plug-and-play ADMM-based algorithm was used to integrate the denoising prior and subspace reconstruction. Results: Impressive SNR-enhancing reconstruction was demonstrated using simulations and in vivo data from different MD-MRSI acquisitions. Impact: A new approach is proposed for multidimensional MR spatiospectral image reconstruction integrating low-dimensional modeling and a prelearned denoiser trained via multidimensional interpolation using only noisy data. Potential impacts on quantitative molecular imaging are demonstrated using different MRSI acquisitions.