Properties of 2D MR image reconstructions with deep neural networks at high acceleration rates
Matthew J. Muckley1, Tullie Murrell2, Alireza Radmanesh3, Florian Knoll4, Zhengnan Huang3, Anuroop Sriram5, Daniel K. Sodickson3, and Yvonne W. Lui3
1Facebook AI Research, New York, NY, United States, 2shaped.ai, New York, NY, United States, 3Radiology, NYU School of Medicine, New York, NY, United States, 4Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany, 5Facebook AI Research, Menlo Park, CA, United States
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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