The role of artificial intelligence in coronary CT angiography.

Coronary CT angiography (CCTA) offers an efficient and reliable tool for the non-invasive assessment of suspected coronary artery disease through the analysis of coronary artery plaque and stenosis. However, the detailed manual analysis of CCTA is a burdensome task requiring highly skilled experts. Recent advances in artificial intelligence (AI) have made significant progress toward a more comprehensive automated analysis of CCTA images, offering potential improvements in terms of speed, performance and scalability.

Spatio-temporal physics-informed learning: A novel approach to CT perfusion analysis in acute ischemic stroke.

CT perfusion imaging is important in the imaging workup of acute ischemic stroke for evaluating affected cerebral tissue. CT perfusion analysis software produces cerebral perfusion maps from commonly noisy spatio-temporal CT perfusion data. High levels of noise can influence the results of CT perfusion analysis, necessitating software tuning.

Physics-informed neural networks for myocardial perfusion MRI quantification.

Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length.