Advanced Techniques in Dynamic Cardiac Ultrasound Imaging for Assessing Left Ventricular Function in Heart Failure Patients

Abstract

Heart failure, a condition characterized by a decline in cardiac pumping capacity, necessitates precise assessment of cardiac function due to its systemic impact on blood circulation. Dynamic cardiac ultrasound imaging serves as a crucial tool for evaluating left ventricular function. The quality of these images directly influences the accuracy of diagnostics and the effectiveness of treatments. Existing cardiac ultrasound image processing technologies face limitations in enhancing details, noise reduction, and capturing dynamic information. This study introduces a novel image processing technique that integrates visual attention mechanisms and generative adversarial networks (GAN) to enhance the details of dynamic cardiac ultrasound images. Additionally, it employs an algorithm based on dynamic contour models for image segmentation and assessment of left ventricular function. The application of these techniques aims to improve the processing quality of cardiac ultrasound images, enabling more accurate assessments of left ventricular function and providing more effective support for the diagnosis and treatment of heart failure patients.