Browsing by Subject "Surgical Robotics"

In surgical robotics, fully-automated tumor removal is an important topic and it includes three main tasks: tissue classification for cancer diagnosis, pathological mapping for tumor localization and tissue resection by using a laser scalpel. Generating a three-dimensional (3D) pathological tissue model with fully non-contact sensors can provide invaluable information to assist surgeons in decision-making and enable the use of surgical robots for efficient tissue manipulation. To collect the comprehensive information of a biological tissue target, robotic laser systems with complementary sensors (e.g., Optical coherence tomography (OCT) sensor, and stereovision) can play important roles in providing non-contact laser scalpels (i.e., cutting laser scalpel) for tissue removal, applying photonics-based sensors for pathological tissue classification (i.e., laser-based endogenous fluorescence), and aligning multi-sensing information to generate a 3D pathological map. However, there are three main challenges with integrating multiple laser-based sensors into the robotic laser system, which includes: 1) Modelling the laser beam transmission in 3D free-space to achieve accurate laser-tissue manipulation under geometric constraints, 2) Studying the complex physics of laser-tissue interaction for tissue differentiation and 3D shape modelling to ensure safe tissue removal, and 3) Integrating information from multiple sensing devices under sensor noise and uncertainties from system calibration.

Targeting these three research problems separately, a computational framework is proposed to provide kinematics and calibration algorithms to control and direct the 3D laser beam through a system with multiple rotary mirrors (to transmit laser beam in free-space) and laser-based sensor inputs. This framework can serve as a base platform for optics-based robotic system designs and solving the motion planning problems related to laser-based robot systems. Simulation experiments have verified the feasibility of the proposed framework and actual experiments have been conducted with an existing robotic laser system on phantom and ex-vivo biological tissues.

To study the complex physics of laser-tissue interaction, a 3D data-driven method is developed to model the geometric relation between the laser energy distribution, laser incident angles, and the tissue deformation resulting from photoablation. The results of the phantom studies have demonstrated the feasibility of applying the trained model for laser crater shape predictions during the surgical planning.

Finally, a research platform, referred as ``TumorMapping", is developed to collect multimodal sensing information from complementary sensors to build a 3D pathological map of a mice tumor surface. This robot system includes a sensor module attached to a 6-DOF robot arm end-effector, based on the laser-induced fluorescence spectroscopy for tissue classification and a fiber couple cutting laser for tissue resection. A benchtop sensor platform is built with an OCT sensor and a stereovision system with two lens camera to collect the tissue information with a non-contact pattern. The robot-sensor and the complementary sensor sub-systems are integrated in a unified platform for the 3D pathological map reconstruction.

In summary, the research contributions include important advancements in laser-based sensor fusion for surgical decision-making which is enabling new capabilities for the use of 3D pathological mapping combined with intelligent robot planning and control algorithms for robotic surgery.

As technology advances the state of medical imaging, the capabilities of surgical tooling has remained stagnant, contributing to a rift between a surgeon’s ability to perceive and their ability to act. At this point in the evolution of surgical tooling, some level of action must be yielded to robotic control. This thesis describes the development and provides an evaluation of a first-prototype device for automated tumor removal. Specifically, the device combines a unique implementation of a three-dimensional scanner with a steerable cutting laser, enabling both sensing and cutting in a platform that can generate 3D images of relatively smooth surfaces to a precision beyond the ability of a human surgeon to act. This device will be used as a research platform to answer the important questions currently standing in the way of bringing automation into the operating room. This work outlines the foundational development of a device that could provide a significant improvement to patient outcomes and reduce operating costs by a magnitude not yet demonstrated in the field of surgical robotics.