Modeling and skill assessment for robot-assisted endovascular catheterization

Endovascular techniques have been embraced as a minimally-invasive treatment approach within different disciplines of interventional radiology and cardiology. The current practice of endovascular procedures, however, is limited by a number of factors including exposure to high doses of X-ray radiation, limited 3D imaging, and lack of contact force sensing and haptic feedback from the endovascular tools and the vascular anatomy. More recently, development of robotic platforms have aimed to improve these practices by removing the operator from the radiation source and increasing the precision and stability of catheter motion with added degrees-of-freedom. Despite their increased application and a growing research interest in this area, many such systems have been designed without considering the natural manipulation skills and ergonomic preferences of the operators. Existing studies on tool interactions and behaviour patterns of operators have been very limited, and presently there is a lack of objective and quantitative metrics for performance and skill evaluation. This research proposes a framework for automated and objective assessment of endovascular skill, by measuring catheter-tissue contact forces and operator force/motion patterns across different skill levels, relating operator tool forces to catheter dynamics and forces exerted on the vasculature, and learning the underlying force and motion patterns that are characteristic of skill. Furthermore, a novel cooperative robotic catheterization system based on 'Learning-from-Demonstration' is developed, by utilizing a learning-based approach for generating optimum motion trajectories from multiple demonstrations of a catheterization task, as well as encoding the higher-level structure of a task as a sequence of primitive motions, to enable semi-autonomous catheter navigation within a collaborative setting. The results provide important insights into improving catheter navigation in the form of assistive or semi-autonomous robotics, and motivate the design of collaborative robots that are intuitive to use, while reducing the cognitive workload of the operator.Open Acces

Panorama ultrasound for navigation and guidance of epidural anesthesia

Epidural anesthesia is a common but challenging procedure in obstetrics and surgery, especially for the obese patient, and can result in complications such as dural puncture and nerve injury. Ultrasound has the potential to significantly improve epidural needle guidance, by being able to depict the spinal anatomy and the epidural space. An ultrasound guidance system is therefore proposed, using a transducer-mounted camera to create 3D panorama images of the spine relative to markings on the skin. Guidance will include depiction of the spinal anatomy, identification of individual vertebrae, and selection of a suitable puncture site, trajectory and depth of needle insertion. The camera tracks the transducer movement using a specialized strip of markers attached to the skin surface. This enables 6-DOF absolute position estimation of the transducer with respect to the patient over the full range of the spine. The 3D panorama image can then be resliced in various parasagittal planes to show either the target epidural spaces or the laminae. The overall accuracy of the panorama reconstruction is validated by measuring inter-feature distances of a phantom of steel beads against measurements obtained from an optical tracking system (Optotrak), resulting in an average error of 0.64 mm between camera and Optotrak. The algorithm is then tested in vivo by creating panorama images from human subjects (n=20), obtaining measurements for depth of insertion to the epidural space, intervertebral spacings, and registration of interspinous gaps to the skin, and validating these against independent measurements by an experienced sonographer. The results showed an average error of 1.69 mm (4.23%) for the depth measurements, average error of 4.44 mm (15.2%) for the interspinous distance measurements, and an average error of 6.65 mm for registering the interspinous gaps to the skin (corresponding to 18.5% of the interspinous distances). Tracking of ultrasound images with respect to the marker is implemented in real time and visualized using the 3D Slicer software package.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat