Analysis of Cardiac Dynamic Global Function

Leon Axel, Mikael Kanski, Amit Jhaveri, Meng Ye, Bangwei Guo, Xiaoxiao He, Dimitris N. Metaxas

Abstract: Although cardiac function is truly vital, and can be adversely affected by many diseases, conventional quantitative global function analysis from images is largely limited to assessing the cardiac volumes at end-diastole and end-systole, due to the time-consuming associated image segmentation. Advances in AI-assisted cardiac image segmentation can potentially enable more detailed analysis of the dynamic changes in cardiac volumes over the cardiac cycle, but there are now no standardized ways to analyze such data. In this work, we propose a systematic approach to the analysis of dynamic global cardiac function from imaging data. We use some representative MRI data to illustrate this approach, but the methods are not limited to MRI. The focus here is on the technical approach, rather than on potential clinical applications.

External IDs:dblp:conf/fimh/AxelKJYGHM25