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Abstract #1877

Deep Learning Enables Accurate Quantitative Volumetric Brain MRI with 2x Faster Scan Times

Long Wang1, Suzie Bash2, Sara Dupont1, Sebastian Magda3, Chris Airriess3, Enhao Gong1, Greg Zaharchuk1,4, Ajit Shankaranarayanan1, and Tao Zhang1
1Subtle Medical Inc, Menlo Park, CA, United States, 2RadNet, Encino, CA, United States, 3CorTechs Labs Inc, San Diego, CA, United States, 4Stanford University, Stanford, CA, United States

3D T1-weighted MRI is valuable for providing high resolution structural information and is commonly used in brain MRI exams despite long scan times. The recent development of deep learning (DL) has shown great potential for scan time reduction. However, the generalizability of DL methods is of concern for actual clinical deployment. In this study, we apply FDA-cleared DL software to accelerate 3D T1-weighted MRI scans by two folds and evaluate the quantification accuracy using FDA-cleared image analysis software. This study provides insight into the generalizability and accuracy of DL in clinical settings.

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