Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

Abstract

AbstractBackgroundView planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice.PurposeExisting approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic‐compatible, annotation‐free system for automatic CMR view planning.MethodsThe system mines the spatial relationship—more specifically, locates the intersecting lines—between the target planes and source views, and trains U‐Net‐based deep networks to regress heatmaps defined by distances from the intersecting lines. On the one hand, the intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. On the other hand, as the spatial relationship is self‐contained in properly stored data, for example, in the DICOM format, the need for additional manual annotation is eliminated. In addition, the interplay of the multiple target planes predicted in a source view is utilized in a stacked hourglass architecture consisting of repeated U‐Net‐style building blocks to gradually improve the regression. Then, a multiview planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. For performance evaluation, the retrospectively identified planes prescribed by the technologists are used as the ground truth, and the plane angle differences and localization distances between the planes prescribed by our system and the ground truth are compared.ResultsThe retrospective experiments include 181 clinical CMR exams, which are randomly split into training, validation, and test sets in the ratio of 64:16:20. Our system yields the mean angular difference and point‐to‐plane distance of 5.68° and 3.12 mm, respectively, on the held‐out test set. It not only achieves superior accuracy to existing approaches including conventional atlas‐based and newer deep‐learning‐based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac‐anatomy‐oriented plane(s) from the body‐oriented scout.ConclusionsThe proposed system demonstrates accurate automatic CMR view plane prescription based on deep learning on properly archived data, without the need for further manual annotation. This work opens a new direction for automatic view planning of anatomy‐oriented medical imaging beyond CMR.