Smart navigation guidance during cardiac interventions

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Nowadays, catheterization is preferred to avoid open heart surgery because it reduces the danger of infection to patient and duration of convalescence. Stent placement, aortic dissection and heart valve repairs are good examples of catheterization procedures. However, it requires far more dexterity from the cardiologist as it involves manipulating a flexible catheter inside a breathing patient's blood vessels. The overarching objective of this research program is to investigate new computational tools for smart navigation guidance during cardiac interventions, thereby alleviating this difficulty. This research program is articulated around 3 specific objectives. The first objective of this research program aims at reconstructing a 3D roadmap of the vascular structure from biplane X--ray angiography. Since two views are not always sufficient for accurate reconstruction, Generative Adversarial Networks (GAN) will be investigated to learn the 3D/2D relationship and to synthesize additional views. The second objective is to develop a new motion model to predict cardiac and respiratory movements during interventions, based on long short-term memory (LSTM) networks, a new class of recurrent neural networks for modelling time series. Motion modelling using neural networks in X--ray angiography has never been done before. Patient subgroups will be considered in the development of the motion model. Finally, modelling of gestures and tasks of the cardiologist will be investigated through reinforcement learning (RL) algorithms. Predicted gestures by RL, yet to be evaluated for catheter manipulation, will be compared against the gestures performed by the cardiologist. This knowledge will improve our understanding of the user's perception during a cardiac intervention, a topic where only a few studies have been published, but which would be very important for the development of remote or fully autonomous cardiac navigation. This research program is innovative since it proposes unique and fundamental engineering contributions in 3D reconstruction, motion modelling and human gesture modelling, which are closely connected. It will bridge an important gap between the cardiologist and robotic assistance and/or remote intervention. My long -term vision is to combine these computational tools with robotic catheter in an end--to--end smart navigation assistance. This will enhance the operator's dexterity and coordination towards even safer use of catheters. This will also pave the way towards a smart remote navigation system using robotic catheters, and consequently, improve safety for the patient, reduce operating and convalescence times and significantly reduce medical costs for the Canadian health care system. This research program will contribute to the training of HQP at all study levels in a medical context, providing trainees with highly marketable skills that are directly transferable to a broad range of industries of importance to Canada.