Abstract

Laparoscopic surgery presents challenges in identifying blood vessels due to lack of tactile feedback. The image-guided laparoscopic surgical tool (IGLaST) integrated with optical coherence tomography (OCT) has potential for in vivo blood vessel imaging; however, distinguishing vessels from surrounding tissue remains a challenge. In this study, we propose utilizing an inter-A-line intensity differentiation-based OCT angiography (OCTA) to improve visualization of blood vessels. By evaluating a tissue phantom with varying flow speeds, we optimized the system's blood flow imaging capabilities in terms of minimum detectable flow and contrast-to-noise ratio. In vivo experiments on rat and porcine models, successfully visualized previously unidentified blood vessels and concealed blood flows beneath the 1 mm depth peritoneum. Qualitative comparison of various OCTA algorithms indicated that the intensity differentiation-based algorithm performed best for our application. We believe that implementing IGLaST with OCTA can enhance surgical outcomes and reduce procedure time in laparoscopic surgeries.

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