  Purpose: A fast data-driven optimization approach, named bias-accelerated
subset 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 of
specific anatomy is available for training and the reconstruction method is
specified, learning which k-space points are more relevant for the specific
anatomy and reconstruction in recovering the non-sampled points. BASS was
tested with four reconstruction methods for parallel MRI based on low-rankness
and sparsity that allow a free choice of the SP. Two datasets were tested, one
of the brain images for high-resolution imaging and another of knee images for
quantitative mapping of the cartilage. Results: BASS, with its low
computational cost and fast convergence, obtained SPs 100 times faster than the
current best greedy approaches. Reconstruction quality increased up to 45\%
with our learned SP over that provided by variable density and Poisson disk
SPs, considering the same scan time. Optionally, the scan time can be nearly
halved without loss of reconstruction quality. Conclusion: Compared with
current approaches, BASS can be used to rapidly learn effective SPs for various
reconstruction methods, using larger SP and larger datasets. This enables a
better selection of efficacious sampling-reconstruction pairs for specific MRI
problems.
