Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial.

Journal: AJNR. American journal of neuroradiology
Published Date: Nov 25, 2021

Abstract

BACKGROUND AND PURPOSE: In this prospective, multicenter, multireader study, we evaluated the impact on both image quality and quantitative image-analysis consistency of 60% accelerated volumetric MR imaging sequences processed with a commercially available, vendor-agnostic, DICOM-based, deep learning tool (SubtleMR) compared with that of standard of care.

Authors

  • S Bash
    RadNet-San Fernando Interventional Radiology, 1510 Cotner Ave., 90025, Los Angeles, CA, USA.
  • L Wang
    Institute of Organ Transplantation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Key Laboratory of Ministry of Health, Key Laboratory of Ministry of Education, Wuhan, China.
  • C Airriess
    Cortechs.ai. (C.A.), San Diego, California.
  • G Zaharchuk
    From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.) gregz@stanford.edu.
  • E Gong
    Electrical Engineering (E.G.), Stanford University and Stanford University Medical Center, Stanford, California.
  • A Shankaranarayanan
    Subtle Medical, 883 Santa Cruz Ave, 94025, Menlo Park, CA, USA.
  • L N Tanenbaum
    RadNet-San Fernando Interventional Radiology, 1510 Cotner Ave., 90025, Los Angeles, CA, USA. nuromri@gmail.com.

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