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Abstract #3567

Oriented Object Detection Convolutional Neural Network for Automated Prescription of Oblique MRI Acquisitions

Eugene Ozhinsky1, 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. The goal of this project was to develop a machine learning pipeline for oblique scan prescription that could be trained on localizer images and metadata from previously acquired MR exams. To achieve that, we have developed a novel multislice rotational region-based convolutional neural network (MS-R2CNN) architecture and evaluated it on dataset of knee MRI exams.

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