We assess the properties of deep neural network-reconstructed brain MR images in the high acceleration regime at factors up to 100. We have three contributions: 1) metrics on model performance from 2- to 100-fold accelerations, 2) a Monte Carlo procedure for scoring the quality of model reconstructions using only subsampled data, and 3) assessment of the acceleration effects on pathology in six cases. Our Monte Carlo procedure can estimate ground truth PSNR with coefficients of determination greater than 0.5 using only subsampled data. Our pathology results were stable in DNN reconstructions up to 8-fold acceleration.
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