  We went below the MRI acceleration factors (a.k.a., k-space undersampling)
reported by all published papers that reference the original fastMRI challenge,
and then considered powerful deep learning based image enhancement methods to
compensate for the underresolved images. We thoroughly study the influence of
the sampling patterns, the undersampling and the downscaling factors, as well
as the recovery models on the final image quality for both the brain and the
knee fastMRI benchmarks. The quality of the reconstructed images surpasses that
of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an
SSIM of 0.956 at x16 acceleration factor. More extreme undersampling factors of
x32 and x64 are also investigated, holding promise for certain clinical
applications such as computer-assisted surgery or radiation planning. We survey
5 expert radiologists to assess 100 pairs of images and show that the recovered
undersampled images statistically preserve their diagnostic value.
