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 Knoll, Daniel K. Sodickson, Yvonne W. Lui

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numbere210313
JournalRadiology: Artificial Intelligence
Issue number6
StatePublished - Nov 2022


  • Deep Learning
  • High Acceleration
  • MRI Reconstruction
  • Out of Distribution
  • Screening

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Artificial Intelligence


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