Keywords: Sparse & Low-Rank Models, Machine Learning/Artificial IntelligenceRecent advances in spatio-temporal subspace reconstruction has enabled accurate reconstruction from highly accelerated scans. Nevertheless, such methods suffer from being computationally intensive due to their iterative nature coupled with the large dimensionality of the problem, especially when imperfection correction is incorporated into the formulation. This abstract proposes deep-learning-initialized compressed sensing (Deli-CS) to accelerate such spatio-temporal reconstruction by providing it with a deep-learning-reconstructed initial solution, reducing the number of iterations required. Using MRF as an example, Deli-CS reconstructs data from a rapid 1-mm isotropic whole-brain TGAS-SPI-MRF with time-segmented B0 correction at 3x faster speed compared to FISTA.
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