Motion compensation for transcatheter aortic valve implantation

先进的医学图像数据可视化工具对于确保经导管主动脉瓣置入术(TAVI)期间的安全导航至关重要。该研究的主要目的是提出一种基于图像的呼吸运动补偿方法，用于TAVI导航制导。TAVI正在成为传统心脏瓣膜开放手术的一种有吸引力的替代方案，它减少了手术时间，改善了患者的康复。TAVI目前是在单视X光血管造影术指导下进行的，不提供任何深度信息。心脏病专家必须评估瓣膜放置的最佳性，并以完美的时机展开瓣膜，所有这些都是在复杂的运动下进行的。自从瓣膜植入的早期阶段起，成像的主要作用就被认识到了。如今，X射线血管成像套件的最新进展允许在太空中自由移动单个射线照相源，以获取任意视角的图像。这项名为X射线旋转血管造影术的技术有望在介入前获取多幅图像进行完整的3D重建。首先，在介入开始时，将使用X射线旋转血管造影术对呼吸暂停患者的主动脉进行三维重建。在介入过程中，将研究基于图谱的3D重建和3D-2D配准，以在考虑呼吸运动的X射线血管成像上产生稳健而准确的主动脉覆盖。这种方法将同时包含患者特定的运动信息和人群特定的运动信息。根据单个X射线血管成像序列，先前计算并在患者参考系中获取的3D体积将通过连续的非刚性3D-2D配准来更新。考虑一种基于动态时间规整的一致性保持方法，用于在2D和3D中对循环呼吸运动模式进行建模和学习。动态时间规整被广泛应用于语音识别领域，用于度量两个时间序列之间的相似性，并有助于将实际运动模式与通用运动图谱中的运动模式进行匹配。基于我们之前在单平面随机运动补偿方面的工作，将从连续配准中学习运动图谱，以可重现的方式从X射线血管造影术(患者特定的)中表示空间运动。此外，术前运动图谱将从模拟数据和真实患者数据集构建，以更好地概括复杂的心血管运动模式(特定人群)。该研究计划的长期目标是为TAVI期间的多模式图像融合生成一个在线导航系统，并在每一次手术的实时成像上动态覆盖几种术前成像方式，例如经食道超声心动图的X射线血管造影。

Advanced visualization tools of medical image data are paramount to ensure safe navigation during Transcatheter Aortic Valve Implantations (TAVI). The main goal of this research program is to propose a methodology for image-based breathing motion compensation for navigation guidance during TAVI. TAVI is becoming an appealing alternative to traditional open­-heart valve surgery, reducing operating time and improving patient recovery. TAVI is currently performed under single view X-­ray angiography guidance, which does not provide any depth information. Cardiologists must evaluate the optimality of valve placement and deploy the valve with perfect timing, all under a complex motion. The predominant role of imaging has been recognized since the early stages of valve implantation. Recent advances in X-­ray angiography suites nowadays allow moving a single radiographic source freely in space to acquire images from any arbitrary views. This technique, called X­-ray rotational angiography is promising to acquire multiple images for a full 3D reconstruction prior to the intervention. First, at the beginning of the intervention, the aorta of the patient in apnea will be reconstructed in 3D using X­-ray rotational angiography. During the intervention, atlas-­based 3D reconstruction and 3D­-2D registration will be investigated to produce robust and accurate overlay of the aorta over X­-ray angiography considering breathing motion. This approach will incorporate both patient­-specific and population-­specific motion information. From a single X­-ray angiography sequence, the 3D volume computed previously and acquired in the patient’s reference frame, will be updated by successive non­-rigid 3D-­2D registration. A consistency preserving approach based on dynamic time warping will be considered for modelling and learning the cyclic respiratory motion patterns in 2D and afterwards in 3D. Dynamic time warping is widely used in the speech recognition community for measuring similarity between two temporal sequences and would contribute to match actual motion pattern with motion patterns from generic motion atlas. A motion atlas, based on our previous work on monoplane stochastic motion compensation, will be learned from successive registration to represent the spatial motion in a reproducible manner from X-­ray angiography (patient­-specific). Furthermore, preoperative motion atlases will be constructed from both simulated data and from real patient datasets to better generalize the complex cardiovascular motion pattern (population-­specific). The long-term objective of this research program is to generate an online navigation system for multimodality image fusion during TAVI, with dynamic overlay of several preoperative imaging modalities on real-time per operative imaging such as X-ray angiography of transoesophagal echocardiography.