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Abstract #1275

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

Suzie Bash1, Long Wang2, Chris Airriess3, Sara Dupont2, Greg Zaharchuk4, Enhao Gong2, Tao Zhang2, Ajit Shankaranarayanan2, and Lawrence Tanenbaum5
1Neuroradiology, RadNet, Woodland Hills, CA, United States, 2Subtle Medical, Menlo Park, CA, United States,, San Diego, CA, United States, 4Stanford University, Stanford, CA, United States, 5RadNet, New York, NY, United States

In this prospective, multireader, multicenter study, we explore the impact of deep learning (DL) enhancement of 60% accelerated 3D T1 weighted brain MR image acquisitions. We found that the DL processed images demonstrated high volumetric quantification accuracy, matched clinical disease status predictability, and provided what readers perceived as superior image quality when compared with the longer standard-of-care exams, suggesting good generalizability, accuracy, and potential utility of DL enhancement in routine clinical settings. The results of this trial support the use of DL enhancement to shorten clinical MR brain examinations, even when additional quantitative tools such as volumetric analysis are applied.

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