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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