Hybrid LiDAR-RGB Surface Reconstruction for Collision Avoidance in Radiotherapy: An Proof‑of‑Concept Phantom Study

Introduction: To evaluate a proof-of-concept three-dimensional surface reconstruction technique using a hybrid LiDAR and RGB sensor system with an open-source, GPU-accelerated pipeline. The goal is to generate photorealistic digital twins of phantom surfaces for integration into radiotherapy collision avoidance workflows. Methods: A portable Intel RealSense sensor was used to acquire synchronized depth and color images. Sensor performance, including depth accuracy, fill rate, and planar root mean square error, was evaluated to determine optimal scan range. A reconstruction pipeline was implemented using the Open3D library with a voxel-based framework, signed distance function integration, ray casting, and color and depth-based simultaneous localization and mapping for pose tracking. Surface meshes were generated using the Marching Cubes algorithm. Validation involved scanning rectangular box phantoms and an anthropomorphic Rando phantom in a single circular motion. Reconstructed models were registered to CT-derived meshes using manual point picking and iterative closest point alignment. Accuracy was assessed using cloud-to-mesh distance metrics and compared to Poisson surface reconstruction. Results: Highest accuracy was observed within the 0.3 to 2.0 meter range. Volume differences for box models were within five millimeters. The Rando phantom showed a registration error of 1.8 millimeters and full overlap with the CT reference. Mean signed distance was minus 0.32 millimeters with a standard deviation of 3.85 millimeters. Conclusions: This technique enables accurate, realistic surface modeling using low-cost, open-source tools and supports future integration into radiotherapy digital twin systems.