Motion compensation for transcatheter aortic valve implantation

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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.