MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising.

We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes an explicit low-dimensional representation of the high-dimensional spatiospectral functions of interest for noise reduction, while the denoiser serves as a complementary spatiotemporal prior to constrain the subspace reconstruction. A self-supervised learning strategy was proposed to train a denoiser that can distinguish the spatio-temporally correlated signals from uncorrelated noise. An iterative reconstruction formalism was developed based on the Plug-and-Play (PnP)-ADMM framework to synergize the subspace constraint, plug-in denoiser and spatiospectral encoding model. We evaluated the proposed method using numerical simulations and in vivo data, demonstrating improved performance over state-of-the-art subspacebased methods. We also provided theoretical analysis on the utility of combining subspace projection and iterative denoising in terms of both algorithm convergence and performance. Our work demonstrated the potential of integrating self-supervised denoising priors and low-dimensional representations for high-dimensional imaging problems.