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Optical coherence tomography (OCT) is a high-resolution non-invasive 3D imaging modality, which has been widely used for biomedical research and clinical studies The robotic-arm-assisted OCT system has the advantages of multiple degrees of freedom and high precision and stability during the imaging process, which can be used to assist doctors in quantitative assessment of vascular status. In this work, we propose a method for system calibration and the pose optimization of the OCT probe to ensure that the probe is in the optimal imaging pose. The system calibration is implemented with the corresponding pixel domain to spatial domain conversion coefficients and Tsai-Lenz method. The pose optimization of the OCT probe can be optimized with image processing. The Laplacian random walk algorithm was used to obtain the skin phantom surface contour to calculating the normal vector p→ of the skin phantom surface. Meanwhile, the contour of vessel phantom was segmented with the convolutional neural network method. The radius vector b→ of vessel phantom was obtained with the segmented results. There are two vectors that determine the attitude of the probe. Vp denotes the unit vector parallel to the optical axis while Ve denotes the unit vector parallel to the B-scan direction. When the OCT probe is in the optimal imaging pose, the vector Vp should be parallel to the p→ and the vector Ve should be parallel to the b→ . We demonstrate an experiment with vessel phantom and skin phantom. It is expected that robotic-arm-assisted OCT imaging can ensure precise data acquisition process assisting the intraoperative decision making.
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