A method for quantifying and automatic grading of musculoskeletal ultrasound superb microvascular imaging based on dynamic analysis of optical flow model

Superb microvascular flow signals in joints are important indicators for evaluating inflammation in arthritis diagnosis. Super Microvascular Imaging (SMI), a musculoskeletal ultrasound technique, captures microvascular signals with enhanced resolution, enabling improved quantitative analysis of joint superb microvascular flow. However, existing musculoskeletal ultrasound imaging predominantly relies on static observations for analyzing these signals, which are heavily influenced by subjective factors, thereby limiting diagnostic accuracy for arthritis. This study introduces a novel quantitative and automated grading method utilizing dynamic analysis through an optical flow model. Real-time dynamic quantification of superb microvascular flow signals is achieved via motion estimation and skeleton extraction based on the optical flow model. The Kappa consistency test evaluates the agreement between the automated grading system and physician assessments, with differences between the two methods analyzed. A total of 47 patient samples were included, comprising 20 males and 27 females (p = 0.307 > 0.05, χ²=1.042). The agreement between the automated grading system and physician assessments reached 70.2%, with a Kappa value of 0.627 (p < 0.001), indicating good consistency. Nonetheless, the system displayed a tendency to high-grade cases of moderate inflammation. The proposed quantitative and automated grading method for superb microvascular flow, based on dynamic analysis through an optical flow model, improves the objectivity and consistency of superb microvascular flow grading and demonstrates significant clinical potential. The method shows strong anti-interference performance in noisy signal environments, representing a promising advancement for non-invasive arthritis diagnosis.