Force sensitive robotic ultrasound for diagnostics and therapy of peripheral arterial disease

Abstract

Peripheral arterial disease (PAD) is one of the most common cardiovascular diseases, affecting more than 230 million people worldwide. It is a form of peripheral atherosclerosis involving the narrowing or blockage of arteries, most commonly in the legs, due to plaque buildup. This reduces blood flow and can lead to tissue damage. The early diagnosis of PAD is crucial to prevent the progression of the disease, maintain the quality of life of patients, and avoid serious complications. The prevalence of PAD increases sharply with age, resulting in an increasing demand for pre- and post-examination of PAD, particularly in industrialized countries with an aging population. Ultrasound imaging (US) is most commonly used as a diagnostic tool, as it enables a fast, non-invasive, radiation-free, and painless examination of vessels. However, the time-consuming and complex nature of US, combined with the growing shortage of clinical staff, is expected to create a gap in its availability in the future. Thus, a force sensitive robotic US system was developed in this thesis, which will enable automatic vascular US examinations for diagnostic purposes, ensuring reliable patient care in the future. Several challenges in the development of such a system have been addressed. This included the investigation of preparatory aspects prior to the examination, such as optimal robot-patient positioning, as well as the safe guidance of the probe during the robotic scanning process.

Firstly, the relationship between the geometry of the probe holder and the positioning of the robot’s base in relation to the patient was investigated. By determining adapted use-case specific probe holder geometries, optimal reachability of the anatomical region to be examined (e.g. the femoral artery) was ensured. The use of an appropriate probe holder geometry strongly increased the number of potential base placements with high reachability, especially in scenarios where obstacles restricted a part of the available space.

Subsequently, a volunteer study was conducted to validate the feasibility of robotic US scans of leg arteries. An adapted interaction control scheme that also accounts for probe contact force was implemented to ensure patient safety. During the scan along the leg, the artery was automatically kept in the center of the US image for optimized image quality. Doppler ultrasound proved to be an effective solution for arterial tracking in this context, allowing for reliable differentiation between arteries and veins based on blood flow visualization. Moreover, additional specific features have been developed to further enhance the system. An approach to automatically adjust the orientation of the probe has been developed to enable automatic scanning of highly curved surfaces. Furthermore, the US gel application, which is essential for US examinations, was automated.

Finally, the accuracy of the robot’s wrench estimation was investigated. Accurate wrench estimation of the robot is essential to ensure the safety of the system, especially in contact with the patient. Thus, a learning approach was proposed in which a neural network was trained on the dependencies between the joint torques in the robot’s axes and the forces and moments acting on the end effector. This strongly increased the accuracy and robustness of the wrench estimation model of the robot, in particular by reducing extreme outliers that occurred in the manufacturer’s model when the robot moved close to singularities.

In summary, this thesis presents novel methods for the development of a robotic US system. The findings of this work represent a step towards future fully automated US examinations. Especially in the context of PAD diagnosis and monitoring, the system has the potential to considerably improve patient care through standardized and automated imaging.