Authors: Suresh Guttikonda, Maximilian Neidhardt, Vidas Raudonis, Alexander Schlaefer
Organizations: Institute of Medical Technology and Intelligent Sys., Hamburg University of Technology, Germany · Interdisciplinary Competence Center for Interface Research, Hamburg, Germany · SustAInLivWork Center of Excellence, Kaunas, Lithuania · Faculty of Electrical and Electronics Eng., Kaunas University of Technology, Lithuania
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.
Figures & tables
Figure 1: OCT Surface Scanning: An OCT scan head ( O ) and a RGB-D camera ( C ) are mounted to a robot ( G ). A custom calibration pattern ( CW ) is used to calibrate the camera and OCT scan head to the robot world coordinate frame ( RW ).
Figure 2: Pipeline to compile a calibration dataset.
Figure 3: Calibration Accuracy: The re-projection error vs (a) distinct OCT-EEF pose pairs acquired at ArUco marker ( j=17 ) (b) ArUco markers ( j=17 ) at various distances from center ArUco marker j=17 .
Figure 4: Spherical Phantom: (a) 6D Surface stitching. (b) 6D surface scanning (top) and corresponding 3D-Translation with artifacts (bottom). (c) Extracted surface points from cross sections in plane 6D scanning (blue) and 3D-translation (red).
Position (\text{,}\mathrm{mm})
Orientation (\text{,}\deg)
x
y
z
r
p
y
Hcg
μ
72.33
−62.51
85.3
68.76
70.41
67.71
σ
0.38
0.40
0.12
0.13
0.06
0.04
Hog
μ
140.28
−9.40
92.15
0.27
91.55
−0.42
σ
0.22
0.04
0.19
0.12
0.24
0.19
Table 1: Hand-in-Eye Calibration Results Camera (top row) and OCT scan head (bottom row)
Figure 5: Soft-Tissue Phantom: 6D and 3D-translation scans acquired on chicken tissue of OCT volumes and RGB-D data. The positions of two exemplary OCT volumes are indicated as A and B.
Optical coherence tomography (OCT) offers compact, contactless, micron-scale imaging suitable for intraoperative guidance, but native OCT volumes are discretely sampled, anisotropic, and currently inefficient for downstream geometric reasoning and robot integration. We present OCTN (pronounced "octane"), an implicit neural representation framework that converts volumetric OCT scans into a continuous, differentiable, and spatially faithful tissue-intensity field. OCTN uses a two-stage hybrid training strategy that combines supervision from acquired voxels with inter-slice interpolations, preserving B-scan fidelity while improving continuity in sparsely sampled regions. For versatility, we first show that OCTN enables fast volumetric reasoning by storing the learned tissue representation natively on the GPU, supporting intensity-based spatial queries with up to 43x speedup over conventional CPU processing. We then demonstrate OCTN-enabled OCT-guided robotic laser surgery where the continuous tissue representation supports implicit surface discovery and surface-constrained path planning via multiple optimization strategies, including Newton- and SGD-based optimization. Next, OCTN enables reconstruction of dense volumetric structure from sparsely acquired B-scans, while reducing acquisition time by 4x and preserving clinically relevant structures. Across the newly generated Duke TissueOCT dataset and public OCT datasets, OCTN achieves robust, high-fidelity reconstruction with PSNR > 30 dB and training time < 10 s, while preserving surface consistency within 10 μm Chamfer distance relative to baseline reconstruction. The TissueOCT dataset and code are publicly available at raprakashvi.github.io/octn
Ravi Prakash, Ryan P. McNabb, Patrick J. Codd +1
Duke University, Durham, NC 27705 USA · Arizona State University, Phoenix, AZ 85287 USA
We present a Gaussian Splatting-based framework for hand-eye calibration of the da Vinci surgical robot. In a vision-guided robotic system, accurate estimation of the rigid transformation between the robot base and the camera frame is essential for reliable closed-loop control. For cable-driven surgical robots, this task faces unique challenges. The encoders of surgical instruments often produce inaccurate proprioceptive measurements due to cable stretch and backlash. Conventional hand-eye calibration approaches typically rely on known fiducial patterns and solve the AX = XB formulation. While effective, introducing additional markers into the operating room (OR) environment can violate sterility protocols and disrupt surgical workflows. In this study, we propose SurgCalib, an automatic, markerless framework that has the potential to be used in the OR. SurgCalib first initializes the pose of the surgical instrument using raw kinematic measurements and subsequently refines this pose through a two-phase optimization procedure under the RCM constraint within a Gaussian Splatting-based differentiable rendering pipeline. We evaluate the proposed method on the public dVRK benchmark, SurgPose. The results demonstrate average 2D tool-tip reprojection errors of 12.24 px (2.06 mm) and 11.33 px (1.9 mm), and 3D tool-tip Euclidean distance errors of 5.98 mm and 4.75 mm, for the left and right instruments, respectively.
Zijian Wu, Shuojue Yang, Yu Chung Lee +3
Robotics and Control Laboratory, University of British Columbia, Vancouver, BC, Canada · Department of Biomedical Engineering, National University of Singapore, Singapore · Division of Otolaryngology – Head and Neck Surgery, University of British Columbia, Vancouver, BC, Canada
In this paper, we present a robotic ultrasound acquisition system that integrates stereo vision, touch-based feedback, and expert-informed strategies to perform autonomous and adaptive abdominal scans. The system records freehand motion and force data from expert radiologists, creating a framework to capture transducer motion, applied forces, and anatomical scanning strategies. This expert data is replayed to replicate characteristic scans with the robot, forming a foundation for further autonomous capabilities. Using stereo vision, the system generates three-dimensional topography maps of the patient's abdomen, which are refined through stiffness measurements at key points to delineate the rib cage boundary. These combined techniques enable the robot to execute two distinct scanning paths: an upward-angled sweep beneath the rib cage to visualize structures near the upper abdomen and a perpendicular sweep across soft tissue regions. A compliant, torque-controlled seven degree-of-freedom robotic manipulator is controlled to maintain consistent probe contact through closed-loop force control over the varied anatomical surfaces. Physical experiments demonstrate that the system achieves high-quality imaging comparable to expert scans while dynamically adapting to patient-specific topographies. Furthermore, the robotic system surpasses expert capabilities by enabling three-dimensional volume acquisition, which enhances diagnostic potential and provides volumetric data for advanced analyses. This work highlights the integration of expert knowledge into autonomous robotic systems and underscores the potential of combining perception-based autonomy with physical reasoning for enhanced diagnostic performance.
Adrian Piedra, R Brooke Jeffrey, Oussama Khatib
Stanford Robotics Laboratory, Computer Science Department, Stanford University, Stanford, CA 94305 USA · Department of Radiology, School of Medicine, Stanford University, Stanford, CA 94305 USA