Smart actuation and sensing for meso-scale surgical robotic systems

This dissertation presents the development of meso-scale surgical robotics based on smart actuation and sensing for minimally invasive surgery (MIS). By replacing conventional straight tools by steerable surgical robots, surgical outcomes can potentially be improved due to more precise, stable, and flexible manipulation. Since bending and torsion are the two fundamental motion forms required by surgical tools to complete general surgical procedures, compact torsion and bending modules, both integrated with intrinsic sensors for motion feedback, have been developed based on shape memory alloy (SMA). The developed actuation and sensing techniques have been applied on a robot for neurosurgical intracerebral hemorrhage evacuation (NICHE) and a steerable catheter for atrial fibrillation (AFib) treatment. The NICHE robot consists of a straight stem, an SMA torsion module, and an SMA bending module as a distal bending tip. By synchronizing the motion of the stem, the bending module, and the torsion module, the robot is capable of tip articulation within the brain to remove hemorrhage effectively through suction and electrocauterization. In addition, a skull-mounted robotic headframe has been developed based on a Stewart platform to manipulate the NICHE robot. The robotic catheter is developed by integrating multiple SMA bending modules with flexible braid reinforced tubing. Polymer 3D-printing is used to fabricate all the structural components due to its relatively low cost, short fabrication period, and capability of fabricating complicated structures with high accuracy. The developed surgical robotic systems have been thoroughly evaluated using phantom or cadaver models under computed tomography (CT) and/or magnetic resonance imaging (MRI) guidance. The imaging-guided experimental studies showed that the developed robotic systems consisting of smart actuation and sensing were compatible with CT and MR imaging.Ph.D

Shared control for natural motion and safety in hands-on robotic surgery

Hands-on robotic surgery is where the surgeon controls the tool's motion by applying forces and torques to the robot holding the tool, allowing the robot-environment interaction to be felt though the tool itself. To further improve results, shared control strategies are used to combine the strengths of the surgeon with those of the robot. One such strategy is active constraints, which prevent motion into regions deemed unsafe or unnecessary. While research in active constraints on rigid anatomy has been well-established, limited work on dynamic active constraints (DACs) for deformable soft tissue has been performed, particularly on strategies which handle multiple sensing modalities. In addition, attaching the tool to the robot imposes the end effector dynamics onto the surgeon, reducing dexterity and increasing fatigue. Current control policies on these systems only compensate for gravity, ignoring other dynamic effects. This thesis presents several research contributions to shared control in hands-on robotic surgery, which create a more natural motion for the surgeon and expand the usage of DACs to point clouds. A novel null-space based optimization technique has been developed which minimizes the end effector friction, mass, and inertia of redundant robots, creating a more natural motion, one which is closer to the feeling of the tool unattached to the robot. By operating in the null-space, the surgeon is left in full control of the procedure. A novel DACs approach has also been developed, which operates on point clouds. This allows its application to various sensing technologies, such as 3D cameras or CT scans and, therefore, various surgeries. Experimental validation in point-to-point motion trials and a virtual reality ultrasound scenario demonstrate a reduction in work when maneuvering the tool and improvements in accuracy and speed when performing virtual ultrasound scans. Overall, the results suggest that these techniques could increase the ease of use for the surgeon and improve patient safety.Open Acces

Integrated navigation and visualisation for skull base surgery

Skull base surgery involves the management of tumours located on the underside of the brain and the base of the skull. Skull base tumours are intricately associated with several critical neurovascular structures making surgery challenging and high risk. Vestibular schwannoma (VS) is a benign nerve sheath tumour arising from one of the vestibular nerves and is the commonest pathology encountered in skull base surgery. The goal of modern VS surgery is maximal tumour removal whilst preserving neurological function and maintaining quality of life but despite advanced neurosurgical techniques, facial nerve paralysis remains a potentially devastating complication of this surgery. This thesis describes the development and integration of various advanced navigation and visualisation techniques to increase the precision and accuracy of skull base surgery. A novel Diffusion Magnetic Resonance Imaging (dMRI) acquisition and processing protocol for imaging the facial nerve in patients with VS was developed to improve delineation of facial nerve preoperatively. An automated Artificial Intelligence (AI)-based framework was developed to segment VS from MRI scans. A user-friendly navigation system capable of integrating dMRI and tractography of the facial nerve, 3D tumour segmentation and intraoperative 3D ultrasound was developed and validated using an anatomically-realistic acoustic phantom model of a head including the skull, brain and VS. The optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue were examined across a wavelength spectrum of 400 nm to 800 nm in order to inform the development of an Intraoperative Hypserpectral Imaging (iHSI) system. Finally, functional and technical requirements of an iHSI were established and a prototype system was developed and tested in a first-in-patient study