3D cinematic reconstructions of cardiovascular CT presented in augmented reality: subjective assessment of clinical feasibility and potential use cases.

Augmented reality (AR) is a new technique enabling interaction with three-dimensional (3D) holograms of cinematic rendering (CR) reconstructions. Research in this field is in its very early steps, and data is scarce. We evaluated image quality, usability, and potential applications of AR in cardiovascular image datasets. Ten CR reconstructions of cardiovascular computed tomography (CT) datasets with complex anatomical abnormalities were presented to six radiologists and three cardiologists first on diagnostic screens and subsequently in AR. Subjective image quality and user experience were rated on 5-point Likert scales to assess usability and potential applications of AR. CR of CT datasets covering multiple images series of the same exam with differing kernels was performed in 143 ± 31 s (mean ± standard deviation); reconstruction of single CT image series took 84 ± 30 s. Mean subjective image quality was excellent, and observers showed high endorsement of the intuitive usability of the AR device and improvement of anatomical comprehensibility. AR devices were expected to have the greatest impact on patient and student education as well as multidisciplinary discussions, with less potential in clinical care. Clinical testing and preclinical implementation of AR seem feasible due to reasonable computation times and intuitive usability even for first-time users. RELEVANCE STATEMENT: The presentation of 3D cinematic rendering in augmented reality provides excellent image quality, facilitating the comprehension of anatomical structures in CT datasets. Concurrently, reasonable computation times and the intuitive usability of augmented reality devices make preclinical implementation and clinical testing feasible. KEY POINTS: 3D cinematic reconstructions presented in augmented reality improve the anatomical comprehensibility of chest CT scans. Augmented reality devices are expected to be highly beneficial in educational settings and multidisciplinary discussions. Usability and computation times are feasible for initial preclinical use cases.