Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI
Alireza Radmanesh, Matthew J. Muckley, Tullie Murrell, Emma Lindsey, Anuroop Sriram, Florian Knöll, Daniel K. Sodickson, Yvonne W. Lui
Abstract
Purpose To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. Materials and Methods In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold along a single phase-encoded direction for two-dimensional [2D] sections) on the fastMRI test set collected at New York University, consisting of 558 image volumes. In a sample of 69 volumes, reconstructions were classified by radiologists for identification of two clinical thresholds: (a) general-purpose diagnostic imaging and (b) potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method. Results Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model’s ability to produce images substantially distinct from the training set, even at 100× acceleration. Conclusion For 2D brain images using deep learning–based reconstruction, maximum acceleration for potential screening was three to four times higher than that for diagnostic general-purpose imaging. Keywords: MRI Reconstruction, High Acceleration, Deep Learning, Screening, Out of Distribution Supplemental material is available for this article. © RSNA, 2022