Cardiac Motion

Cardiac motion analysis focuses on accurately quantifying and mapping the movement of the heart muscle throughout the cardiac cycle, crucial for diagnosing and treating cardiovascular diseases. Current research heavily utilizes deep learning, employing architectures like optical flow networks, variational autoencoders, and implicit neural representations to analyze data from various imaging modalities (echocardiography, MRI, CT). These advancements aim to improve the accuracy and efficiency of myocardial motion tracking, enabling more precise diagnoses and personalized treatment strategies for conditions like myocardial infarction and arrhythmogenic right ventricular cardiomyopathy. The development of robust and automated methods holds significant promise for improving patient care and advancing our understanding of cardiac function.