Minimally invasive operations require surgeons to make difficult cuts to blood vessels and other tissues with impaired tactile and visual feedback. This leads to inadvertent cuts to blood vessels hidden beneath tissue, causing serious health risks to patients and a non-reimbursable financial burden to hospitals. Intraoperative imaging technologies have been developed, but these expensive systems can be cumbersome and provide only a high-level view of blood vessel networks. In this research, we propose a lean reflectance-based system, comprised of a dual wavelength LED, photodiode, and novel signal processing algorithms for rapid vessel characterization. Since this system takes advantage of the inherent pulsatile light absorption characteristics of blood vessels, no contrast agent is required for its ability to detect the presence of a blood vessel buried deep inside any tissue type (up to a cm) in real time. Once a vessel is detected, the system is able to estimate the distance of the vessel from the probe and the diameter size of the vessel (with a resolution of ~2mm), as well as delineate the type of tissue surrounding the vessel. The system is low-cost, functions in real-time, and could be mounted on already existing surgical tools, such as Kittner dissectors or laparoscopic suction irrigation cannulae. Having been successfully validated ex vivo, this technology will next be tested in a live porcine study and eventually in clinical trials.
Amal Chaturvedi, Shetha A. Shukair, Paul Le Rolland, Mayank Vijayvergia, Hariharan Subramanian, and Jonathan W. Gunn, "Optics based signal processing methods for intraoperative blood vessel detection and quantification in real time
(Conference Presentation)," Proc. SPIE 9707, Dynamics and Fluctuations in Biomedical Photonics XIII, 97070Q (Presented at SPIE BiOS: February 15, 2016; Published: 26 April 2016); https://doi.org/10.1117/12.2213688.4848677969001.
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