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.

Artificial Intelligence Applications in Cardiac CT Imaging for Ischemic Disease Assessment.

Artificial intelligence (AI) has transformed medical imaging by detecting insights and patterns often imperceptible to the human eye, enhancing diagnostic accuracy and efficiency. In cardiovascular imaging, numerous AI models have been developed for cardiac computed tomography (CCT), a primary tool for assessing coronary artery disease (CAD). CCT provides comprehensive, non-invasive assessment, including plaque burden, stenosis severity, and functional assessments such as CT-derived fractional flow reserve (FFRct).

Association of thoracic aortic calcium with incident cardiovascular disease and all-cause mortality across the spectrum of coronary artery calcium burden.

Background: Calcification of the ascending and/or descending thoracic aorta is easily measured via non-contrast cardiac computed tomography (CT), commonly performed for quantification of coronary artery calcium (CAC). We assessed whether thoracic aortic calcium (TAC) further improves long-term cardiovascular disease (CVD) risk stratification beyond CAC alone.

Methods: Cardiac CT was performed among 6,783 asymptomatic Multi-Ethnic Study of Atherosclerosis participants at baseline.

Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.

Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation. This scientific statement, endorsed by several societies in the field, provides an overview of the current landscape and challenges of AI applications in cardiac CT and MRI.