Myocardium Tissue Characteristics Quantification in Echocardiography

Echocardiography is a preemptive imaging modality for assessing cardiac conditions in emergencies. However, its operator-dependent nature presents critical limitations. To address such challenges, we introduce a learning-based quantitative ultrasound (QUS) system that reconstructs tissue biomechanical properties for broad-and-deep area with a sector probe for echocardiography. A ROI-aware dynamic patch-wise network is employed, efficiently decoding received data by guiding distinct weights based on the position of the left ventricle wall. In addition, the proposed MTGC module compensates for errors in reconstructing attenuation coefficients stemming from variations in signal attenuation at different depths due to tissue heterogeneity. The proposed network demonstrated a performance improvement of 2.34% compared to the baseline in the ablation study, and we validated its effectiveness in diagnosing heart disease through phantom and ex-vivo test. A series of experiments show the potential for a new diagnostic paradigm based on quantitative indices in echocardiography.