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12 March 2007 A new contrast-assisted method in microcirculation volumetric flow assessment
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Abstract
Microcirculation volumetric flow rate is a significant index in diseases diagnosis and treatment such as diabetes and cancer. In this study, we propose an integrated algorithm to assess microcirculation volumetric flow rate including estimation of blood perfused area and corresponding flow velocity maps based on high frequency destruction/contrast replenishment imaging technique. The perfused area indicates the blood flow regions including capillaries, arterioles and venules. Due to the echo variance changes between ultrasonic contrast agents (UCAs) pre- and post-destruction two images, the perfused area can be estimated by the correlation-based approach. The flow velocity distribution within the perfused area can be estimated by refilling time-intensity curves (TICs) after UCAs destruction. Most studies introduced the rising exponential model proposed by Wei (1998) to fit the TICs. Nevertheless, we found the TICs profile has a great resemblance to sigmoid function in simulations and in vitro experiments results. Good fitting correlation reveals that sigmoid model was more close to actual fact in describing destruction/contrast replenishment phenomenon. We derived that the saddle point of sigmoid model is proportional to blood flow velocity. A strong linear relationship (R = 0.97) between the actual flow velocities (0.4-2.1 mm/s) and the estimated saddle constants was found in M-mode and B-mode flow phantom experiments. Potential applications of this technique include high-resolution volumetric flow rate assessment in small animal tumor and the evaluation of superficial vasculature in clinical studies.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sheng-Yi Lu, Yung-Sheng Chen, and Chih-Kuang Yeh "A new contrast-assisted method in microcirculation volumetric flow assessment", Proc. SPIE 6513, Medical Imaging 2007: Ultrasonic Imaging and Signal Processing, 651316 (12 March 2007); https://doi.org/10.1117/12.708114
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