A Dual-Domain Framework with Wavelet Attention for Cardiac Ultrasound Image Quality Assessment

Automated quality assessment of cardiac ultrasound images is crucial for ensuring diagnostic accuracy and clinical decision-making reliability. Hospitals face significant challenges in efficiently screening ultrasound image quality, where manual expert review is time-consuming and subjective. However, dedicated methods for ultrasound image quality assessment remain scarce, with most adapted from natural image quality metrics that fail to capture clinically relevant diagnostic factors. In this paper, we propose a novel dual-domain framework that models both spatial anatomical features and frequency-domain spectral characteristics using specialized neural modules. Our approach incorporates cardiac-specific attention mechanisms and wavelet-based artifact detection to enable comprehensive, clinically aligned evaluation. Extensive experiments on a largescale clinical dataset demonstrate the superiority of our method, achieving 88.1 % overall accuracy, a macro-averaged F1-score of 88.1 %, and 100 % precision and recall in detecting diagnostically unacceptable images. The proposed framework is designed to meet clinical reliability standards, paving the way for safe integration into diagnostic workflows.