Analysis of cardiac dynamic global function.

Objectives: 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, and the associated ejection fraction, 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, in clinically practical times, 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. Design: We use some cardiac magnetic resonance imaging (CMR) data here 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. Representative short-axis cine CMRs from 19 normal subjects and 22 patients with clinical diagnosis of "heart failure with preserved ejection fraction" were analyzed for ventricular volumes over the cardiac cycle. The resulting data were used to calculate a set of dynamic global function variables. Results: A set of representative measures of the timing and rates of ventricular emptying and filling is promising as compact means to characterize dynamic global function. Conclusions: More efficient cardiac segmentation offers the potential to characterize dynamic global cardiac function, through a set of representative measures of the timing and rates of ventricular emptying and filling.