Smart navigation guidance during cardiac interventions

目前，导管置入术是避免心内直视手术的首选方法，因为导管置入术可以减少患者感染的危险，缩短恢复期。支架置入、主动脉夹层和心脏瓣膜修复都是导管置入术的好例子。然而，它对心脏病专家的要求要灵活得多，因为它涉及到在呼吸病人的血管内操作一个灵活的导管。本研究计划的总体目标是研究心脏干预期间智能导航指导的新计算工具，从而减轻这一困难。该研究计划围绕三个具体目标进行阐述。本研究计划的第一个目标是通过双翼X线血管造影重建血管结构的3D路线图。由于两个视图并不总是足以进行准确的重建，因此将研究生成对抗网络（GAN）来学习3D/2D关系并合成其他视图。第二个目标是开发一种新的运动模型来预测干预期间的心脏和呼吸运动，该模型基于长短期记忆（LSTM）网络，这是一种用于建模时间序列的新型循环神经网络。在X射线血管造影中使用神经网络进行运动建模以前从未做过。患者亚组将在运动模型的发展中被考虑。最后，将通过强化学习（RL）算法研究心脏病专家的手势和任务建模。RL预测的手势，尚未对导管操作进行评估，将与心脏病专家的手势进行比较。这些知识将提高我们对心脏干预期间用户感知的理解，这是一个只有少数研究发表的主题，但这对于远程或完全自主心脏导航的发展非常重要。这个研究项目是创新的，因为它在三维重建、运动建模和人体手势建模方面提出了独特的基础工程贡献，这三者密切相关。它将弥合心脏病专家与机器人辅助和/或远程干预之间的重要差距。我的长期愿景是将这些计算工具与机器人导管结合起来，实现端到端的智能导航辅助。这将提高操作者的灵活性和协调性，甚至更安全地使用导尿管。这也将为使用机器人导尿管的智能远程导航系统铺平道路，从而提高患者的安全性，减少手术和康复时间，并显着降低加拿大医疗保健系统的医疗成本。该研究项目将有助于在医学背景下对所有学习水平的HQP进行培训，为学员提供高度市场化的技能，这些技能可直接转移到对加拿大重要的广泛行业。

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.