Path planning for semi-automated simulated robotic neurosurgery

Abstract

This paper considers the semi-automated robotic surgical procedure for removing the brain tumor margins, where the manual operation is a tedious and time-consuming task for surgeons. We present robust path planning methods for robotic ablation of tumor residues in various shapes, which are represented in point-clouds instead of analytical geometry. Along with the path plans, corresponding metrics are also delivered to the surgeon for selecting the optimal candidate in the automated robotic ablation. The selected path plan is then executed and tested on RAVEN™ II surgical robot platform as part of the semi-automated robotic brain tumor ablation surgery in a simulated tissue phantom.

Semi‐autonomous image‐guided brain tumour resection using an integrated robotic system: A bench‐top study

“…Table lists the results from 10 trial runs. Each trial used one of the four path patterns to ablate the tumour residue based on its size and shape: zigzag, contour parallel, a mixed pattern of the two as well as spot‐wise suction of small tumours . Seven out of 10 trials were successful.…”

“…From the achieved point‐clouds of tumour residues, corresponding ablation plans are generated using the path planner described in . The robot will then execute one of the plans approved by the user (surgeon) through a terminal input.…”

“…From the achieved point-clouds of tumour residues, corresponding ablation plans are generated using the path planner described in. 17 The robot will then execute one of the plans approved by the user (surgeon) through a terminal input. We combined two types of vision sources as feedback to improve the overall ablation accuracy: (1) In these cases, the generated ablation path becomes inaccurate.…”

“…Algorithmic details of subtasks will be addressed in the following sections. In earlier papers, we have shown the feasibility of modeling the ablation procedure using the Behavior Tree framework and creating a 3D mosaic using a set of 2D reflectance SFE images, as well as the validity of a surgical coverage path planner based on the known point‐cloud of tumour residues individually on the robotic system and SFE imaging system respectively. The RAVEN™ II surgical robotic software is implemented in C/C++ on a real‐time patched Linux system, while the SFE imaging system was originally developed in LABVIEW ® and MATLAB ® on a Windows system that required manual operations for image acquisition.…”

Semi-autonomous electrosurgery for tumor resection using a multi-degree of freedom electrosurgical tool and visual servoing

“…This setup enabled the da Vinci™ Surgical System to autonomously detect, map, and remove pseudo-tumors in phantom tissues [27]. The RAVEN II surgical system, when equipped with stereo tracking and near-infrared (NIR) imaging, could detect pseudo neuroblastoma, generate a surgical plan, and execute semi-autonomous tumor ablation [28]. While each technology demonstrates proof of concept for autonomous cutting, they do not consider tissue motion, deformation, and textural similarity in clinical applications.…”