Statistical modeling of blood and tissue signatures using ultrasonic color flow imaging

Conventional color flow processing is primarily optimized for qualitative visualization of flow dynamics, limiting its diagnostic use in regions where vascular structures are small relative to the ultrasound beamwidth. Leveraging the statistical properties of color flow data may provide a pathway toward quantitative discrimination between blood and tissue signals. This could enhance detection of vascular abnormalities, improve diagnostic accuracy, and support monitoring in diseases with small hemodynamic changes. Experimental data was obtained using a clinical GE LOGIQ 9 ultrasound system with a 10L linear array probe (3.75 MHz) positioned on an in-house made half-space flow phantom with the focus located at 3 cm depth. The simulation data obtained from Field II used a setup analogous to the experimental settings. Theoretical probability density function of ultrasound color flow power was derived using a gamma distribution. Shape parameters for blood and tissue were estimated using maximum likelihood estimation (MLE) in both simulation and experimental data. Color flow power was found to follow the gamma distribution in both simulation and experimental data. The estimated shape parameters aligned with theoretical predictions and distinguished between blood and tissue. Estimated shape parameters are less than or equal to 1 for tissue samples and greater than 1 for blood samples. This study presents a statistical modeling approach to enhance blood-tissue differentiation in color flow ultrasound, enabling blood characterization and perfusion quantification for improved detection and monitoring of vascular abnormalities.