The relatively small fields of view of optical coherence tomography (OCT) and OCT angiography (OCTA) not only limit their clinical applications for disease diagnostics, but also lead to incorrect diagnoses. To achieve ultrawide-field OCT/OCTA imaging, two strategies are typically employed: one involves enhancing the optical design to expand the imaging field of view, which is complicated, expensive, and may introduce distortions that degrade the image quality; while the other involves performing multiple local scans with a flexible probe (such as a handheld probe), which can introduce motion artifacts. To obtain large-scale, high-quality OCT images, both flexible and stable scanning mechanisms besides high-precision image registration techniques are essential. Accordingly, in this study, a large-scale OCT imaging technique based on a 6-joint robotic arm is explored. First, the OCT probe is loaded and moved to multiple local regions for optical scanning. The resulting images are then precisely stitched using a dual-cross-correlation-based translation and rotation registration (DCCTRR) algorithm considering the coordinate information of the robotic arm. This research can serve as a valuable reference for improving the clinical applications of OCT, providing methods to enhance both the user experience and the overall effectiveness of OCT system techniques.

A home-built spectral-domain OCT (SDOCT) system (Fig. 1) and a commercially available 6-joint robotic arm are adopted to test the proposed technique. The transformation matrix from the robot end effector to the OCT coordinate system is calculated using singular value decomposition (SVD). Consecutive local OCT scanning is performed using a home-developed C++ application, and the target pose is converted to a joint pose via an inverse kinematic calculation for robot pose control. To complete a large-scale scan of a chicken breast, 5×5 square grids covering ~8.2 mm×8.2 mm are set, and the overlap ratio can be flexibly adjusted for the registration algorithm mentioned above. Finally, 25 local OCT images are obtained and used as stitches to validate the performance of the proposed technique.

To determine the coordinate transformation from the robotic arm end effector to the OCT coordinate system, the displacement of the steel ball center is measured during the three positional changes of the mechanical arm (Table 1). Regarding OCT image registration, the registration accuracy of 91.07% is achieved using the DCCTRR algorithm, significantly outperforming the kinematic matrix method with the accuracy of 77.20% (Fig. 4). Using the transformed information from the mechanical arm and the DCCTRR method, a large-scale frontal structural image is obtained from the registered 5×5 grid of local chicken breast OCT images (Fig. 6).

In this study, the use of a 6-joint robotic arm to load a high-resolution OCT system probe is explored with the aim of achieving large-scale, high-resolution imaging. Because the positioning accuracy of the robotic arm is lower than the OCT imaging resolution, post-image registration (using the DCCTRR algorithm) is required for high-precision image registration. Compared with manual operations, this approach can greatly improve the imaging field without introducing motion artifacts. In summary, robotic arms, image-registration algorithms, and flexible OCT probes are considered in this work to achieve large-scale high-resolution imaging. We believe that this research can serve as a valuable reference for improving the clinical applications of OCT, providing methods to enhance both the user experience and the overall effectiveness of OCT system techniques.