Developing a medical robot for MR guided cardiac catheterization

dc.contributor.advisorMuthurangu, Vivek
dc.contributor.authorAlmutairi, Abdullah
dc.date.accessioned2024-12-08T09:59:39Z
dc.date.issued2024
dc.description.abstractCardiac catheterization involves the insertion of a needle into the veins, enabling physicians to obtain images of the heart without invasive surgery. This procedure, therefore, plays a key role in the diagnosis and treatment of various heart diseases. In recent years, there has been widespread adoption of robotics in surgical procedures, whereby some of the benefits include efficiency, a faster operational speed, and a high rate of action reproducibility. The primary objective of this study was to evaluate the application of behavioural cloning in training robotic systems to perform robotic magnetic resonance–guided catheterization on 3D-printed heart models. Six 3D heart models were printed, and the time taken to perform the catheterization process was measured. The data collection process consisted of manual catheterization, catheterization using a joystick, and simulations of both processes. The results indicated that the manual catheterization process was faster than the robotic one. Nevertheless, the success of the robotic-assisted simulation indicates that it is possible to use behavioural cloning to train the robotic systems to perform catheterization. This study demonstrates that behavioural cloning can be effectively adopted in the catheterization process, whereby learning models can be developed for conducting catheterization procedures.
dc.format.extent53
dc.identifier.urihttps://hdl.handle.net/20.500.14154/74044
dc.language.isoen
dc.publisheruniversity college london
dc.subjectAi Cardiac catheteriztion
dc.subjectCardiac Robot
dc.subjectmachine learning
dc.titleDeveloping a medical robot for MR guided cardiac catheterization
dc.typeThesis
sdl.degree.departmentInstitute of Cardiovascular Science
sdl.degree.disciplinecardiac catheteriztion
sdl.degree.grantoruniversity college london
sdl.degree.nameMaster of Science

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