6D Robotic OCT Scanning of Curved Tissue Surfaces
This paper tackles robotic optical coherence tomography (OCT) scanning of curved tissue surfaces, addressing the limitation that existing approaches restrict motion to pure translations to avoid challenging hand-eye calibration. The core contribution is a custom ChArUco calibration pattern enabling full six-degree-of-freedom hand-eye calibration, allowing the OCT probe to rotate and follow curved surfaces. This matters because pure translational scanning accumulates registration errors on curved geometries, whereas full 6D motion enables accurate, large-area surface reconstruction.
The paper presents a technically sound calibration pipeline with clear quantitative evidence of superiority over translation-only scanning. The custom ChArUco phantom design enables simultaneous camera and OCT calibration without volume stitching during acquisition. The results convincingly demonstrate that 6D scanning eliminates artifacts on curved surfaces, achieving a sphere radius error of 0.35 mm versus 8.26 mm for pure translation. However, the evaluation is restricted to phantom data, and the manual pose refinement during scanning undermines claims of full automation.
The key insight—using ArUco markers detectable in both RGB-D images and OCT en-face projections—is practical and well-executed. The calibration avoids error accumulation by not requiring volume stitching during calibration data acquisition. The quantitative validation is thorough: reprojection errors of 0.36(0.25) mm demonstrate sub-millimeter accuracy, and the spherical phantom test provides a rigorous geometric ground truth that clearly exposes the limitations of translation-only scanning.
The evaluation is limited to phantom studies only, with no ex-vivo or in-vivo tissue validation despite the clinical motivation. The calibration accuracy degrades with distance from the board center (maximum error 0.53 mm), suggesting a limited effective working volume. The reported manual pose refinement ("Poses are refined manually to capture sufficient surface information") introduces subjectivity and limits reproducibility. Additionally, the method requires approximately 25 OCT volumes for calibration convergence, which may be time-consuming ($\sim$2 minutes acquisition time alone) for a clinical workflow.
The evidence strongly supports the claim that 6D scanning outperforms 3D-translation for curved surfaces. The sphere fitting experiment provides an objective geometric benchmark showing nearly 25x improvement in radius estimation error. The chicken phantom qualitatively demonstrates failure modes of translation-only scanning on complex surfaces. However, the paper lacks comparison with alternative hand-eye calibration methods (e.g., those using point-based phantoms or simultaneous camera-OCT calibration without ArUco markers), making it difficult to assess whether the fiducial-based approach is optimal.
Hardware and calibration phantom specifications are detailed (IRB 120 robot, RealSense D405, Thorlabs PR3-13-AL, 10×7 checkerboard with 10 mm squares, 5×5 mm ArUco markers). The mathematical notation for the hand-eye calibration using homogeneous transforms $\mathbf{H}_{cg}$ and $\mathbf{H}_{og}$ is clearly defined. However, no code, datasets, or calibration parameters (e.g., ArUco dictionary type, PnP solver algorithm) are provided. The manual pose refinement step is not quantified or standardized, representing a significant barrier to exact reproduction of the scanning results.
Optical coherence tomography (OCT) is a non-invasive volumetric imaging modality with high spatial and temporal resolution. For imaging larger tissue structures, OCT probes need to be moved to scan the respective area. For handheld scanning, stitching of the acquired OCT volumes requires overlap to register the images. For robotic scanning and stitching, a typical approach is to restrict the motion to translations, as this avoids a full hand-eye calibration, which is complicated by the small field of view of most OCT probes. However, stitching by registration or by translational scanning are limited when curved tissue surfaces need to be scanned. We propose a marker for full six-dimensional hand-eye calibration of a robot mounted OCT probe. We show that the calibration results in highly repeatable estimates of the transformation. Moreover, we evaluate robotic scanning of two phantom surfaces to demonstrate that the proposed calibration allows for consistent scanning of large, curved tissue surfaces. As the proposed approach is not relying on image registration, it does not suffer from a potential accumulation of errors along a scan path. We also illustrate the improvement compared to conventional 3D-translational robotic scanning.
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