Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI

Radmanesh A, Muckley MJ, Murrell T, Lindsey E, Sriram A, Knoll F, Sodickson DK, Lui YW (2022)


Publication Type: Journal article, Original article

Publication year: 2022

Journal

Book Volume: 4

Article Number: e210313

Journal Issue: 6

DOI: 10.1148/ryai.210313

Abstract

Purpose: To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for generalpurpose 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.

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How to cite

APA:

Radmanesh, A., Muckley, M.J., Murrell, T., Lindsey, E., Sriram, A., Knoll, F.,... Lui, Y.W. (2022). Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI. Radiology: Artificial Intelligence, 4(6). https://dx.doi.org/10.1148/ryai.210313

MLA:

Radmanesh, Alireza, et al. "Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI." Radiology: Artificial Intelligence 4.6 (2022).

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