Automated prescription of left ventricular outflow tract and aortic valve views with a U-Net model

Motivation: Cardiac magnetic resonance examinations require precise positioning of double-oblique planes. This task requires a high level of expertise, limiting the availability of CMR. Goal(s): This study aimed to evaluate a deep learning model to automate the prescription of left ventricular outflow tract and aortic valve views. Approach: A U-Net model was implemented and trained on manually annotated bSSFP Cine images to locate the aortic valve insertion points on 3CH and LVOT views. Results: Trained on >3000 images with 12x augmentation, the model achieved comparable accuracy to expert operators, with mean angle discrepancies of 13.4° for LVOT and 10.2° for AoV. Impact: The proposed model can replicate the prescription skills of expert CMR operators, for LVOT and AoV views. Ongoing work will assess its impact on clinical workflows and validate its performance in diverse patient populations to ensure robustness in practice.