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Abstract #1518

Towards Clinical Translation of Machine Learning-based Automated Prescription of Spine MRI Acquisitions

Eugene Ozhinsky1, Felix Liu1, Valentina Pedoia1, and Sharmila Majumdar1
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States


High quality scan prescription that optimally covers the area of interest with scan planes aligned to relevant anatomical structures is crucial for error-free radiologic interpretation. In this study we used images and metadata from previously acquired examinations of lumbar spine to train machine learning-based automated prescription models without the need of any manual annotation or feature engineering. The automated prescription pipeline was integrated with the scanner console software and evaluated in healthy volunteer experiments. This study demonstrates the feasibility of using oriented object detection-based pipelines on the scanner for automated prescription of lumbar spine acquisitions.

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