See, Plan, Cut: MPC-Based Autonomous Volumetric Robotic Laser Surgery with OCT Guidance

TL;DR

This paper introduces RATS, an OCT-guided robotic system for autonomous volumetric soft tissue resection, combining advanced imaging, laser calibration, and model predictive control for high-precision surgery.

Contribution

It presents a novel integrated platform with OCT-guided calibration, a super-Gaussian laser-tissue interaction model, and a sampling-based MPC framework for autonomous soft tissue resection.

Findings

Achieved OCT-to-laser calibration accuracy of 0.161mm on tissue phantoms.

Super-Gaussian LTI model outperformed Gaussian baselines with RMSE of 0.231mm.

Closed-loop MPC improved resection accuracy and preserved subsurface structures.

Abstract

Robotic laser systems offer the potential for sub-millimeter, non-contact, high-precision tissue resection, yet existing platforms lack volumetric planning and intraoperative feedback. We present RATS (Robot-Assisted Tissue Surgery), an intelligent opto-mechanical, optical coherence tomography (OCT)-guided robotic platform designed for autonomous volumetric soft tissue resection in surgical applications. RATS integrates macro-scale RGB-D imaging, micro-scale OCT, and a fiber-coupled surgical laser, calibrated through a novel multistage alignment pipeline that achieves OCT-to-laser calibration accuracy of 0.161+-0.031mm on tissue phantoms and ex vivo porcine tissue. A super-Gaussian laser-tissue interaction (LTI) model characterizes ablation crater morphology with an average RMSE of 0.231+-0.121mm, outperforming Gaussian baselines. A sampling-based model predictive control (MPC) framework operates directly on OCT voxel data to generate constraint-aware resection trajectories with closed-loop feedback, achieving 0.842mm RMSE and improving intersection-over-union agreement by 64.8% compared to feedforward execution. With OCT, RATS detects subsurface structures and modifies the planner's objective to preserve them, demonstrating clinical feasibility.