Automatic OCT Scanning of Moving Soft Tissue Using Continuum Endoscopic Robots

Optical Coherence Tomography (OCT) offers real-time, high-resolution imaging of tissue internal structures, presenting a potential enhancement over conventional endoscopic examination of the digestive system surface. However, miniaturized OCT probes typically present challenges with a limited field of view and require precise positioning to optimize tissue visualization. This research addresses the challenge by leveraging robotizing a surgical endoscope and a steerable OCT probe to provide closed-loop scanning of larger areas in the presence of tissue motion. We propose a robotic multi-pullback technique, supported by real-time, tactile feedback from OCT images. Deep learning algorithms are employed for OCT image stabilization and segmentation, enabling autonomous robotic volumetric OCT scanning. Compared to previous works, our closed-loop control approach considers, in addition to the probe flexibility, also the movements and deformations of the tissue during the robotic scanning. For experimental validation, we designed a moving soft tissue phantom with force sensing and compared the proposed method with fixed-angle approaches. Our method consistently maintained minimal contact forces (around 50 mN) and clear tissue visibility for tissues with different stiffness and motion patterns. Resulting imaging quality was further validated using a multi-layer optical phantom under dynamic conditions.