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Purpose: A fast data-driven optimization approach, named bias-acceleratedsubset selection (BASS), is proposed for learning efficacious sampling patterns(SPs) with the purpose of reducing scan time in large-dimensional parallel MRI.Methods: BASS is applicable when Cartesian fully-sampled k-space data ofspecific anatomy is available for training and the reconstruction method isspecified, learning which k-space points are more relevant for the specificanatomy and reconstruction in recovering the non-sampled points. BASS wastested with four reconstruction methods for parallel MRI based on low-ranknessand sparsity that allow a free choice of the SP. Two datasets were tested, oneof the brain images for high-resolution imaging and another of knee images forquantitative mapping of the cartilage. Results: BASS, with its lowcomputational cost and fast convergence, obtained SPs 100 times faster than thecurrent best greedy approaches. Reconstruction quality increased up to 45\%with our learned SP over that provided by variable density and Poisson diskSPs, considering the same scan time. Optionally, the scan time can be nearlyhalved without loss of reconstruction quality. Conclusion: Compared withcurrent approaches, BASS can be used to rapidly learn effective SPs for variousreconstruction methods, using larger SP and larger datasets. This enables abetter selection of efficacious sampling-reconstruction pairs for specific MRIproblems.

Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems