Robust Live Tracking of Mitral Valve Annulus for Minimally-Invasive Intervention Guidance

Mitral valve (MV) regurgitation is an important cardiac disorder that affects 2-3% of the Western population. While valve repair is commonly performed under open-heart surgery, an increasing number of transcatheter MV repair (TMVR) strategies are being developed. To be successful, TMVR requires extensive image guidance due to the complexity of MV physiology and of the therapies, in particular during device deployment. New trans-esophageal echocardiography (TEE) enable real-time, full-volume imaging of the valve including 3D anatomy and 3D color-Doppler flow. Such new transducers open a large range of applications for TMVR guidance, like the 3D assessment of the impact of a therapy on the MV function. In this manuscript we propose an algorithm towards the goal of live quantification of the MV anatomy. Leveraging the recent advances in ultrasound hardware, and combining machine learning approaches, predictive search strategies and efficient image-based tracking algorithms, we propose a novel method to automatically detect and track the MV annulus over very long image sequences. The method was tested on 12 4D TEE annotated sequences acquired in patients suffering from a large variety of disease. These sequences have been rigidly transformed to simulate probe motion. Obtained results showed a tracking accuracy of 4.04mm mean error, while demonstrating robustness when compared to purely image based methods. Our approach therefore paves the way towards quantitative guidance of TMVR through live 3D valve modeling.