Abstract
Mitral regurgitation (MR), characterized by reverse blood flow during systole, is one of the most common valvular heart diseases. It typically requires treatment via surgical (mitral valve replacement or repair) or percutaneous approaches (e.g., MitraClip). To assist clinical diagnosis and assessment, we propose a learning-based framework to automatically detect and quantify mitral regurgitation from transthoracic echocardiography (TTE), which is usually the initial method to evaluate the cardiac and valve function. Our method leverages both anatomical (B-Mode) and hemodynamical (Color Doppler) information by extracting 3D features on multiple channels and selecting the most relevant ones by a boosting-based approach. Furthermore, the proposed framework provides an automatic modeling of mitral valve structures, such as the location of the regurgitant orifice, the mitral annulus, and the mitral valve closure line, which can be used to assist medical treatment or interventions. To demonstrate the performance of our method, we evaluate the system on a clinical dataset acquired from MR patients. Preliminary results agree well with clinical measurements in a quantitative manner.
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References
Maisano, F., Torracca, L., Oppizzi, M., Stefano, P.L., D’Addario, G., Canna, G.L., Zogno, M., Alfieri, O.: The edge-to-edge technique: a simplified method to correct mitral insufficiency. Euro. J. Cardio-Thoracic Surgery 13(3), 240–245 (1998)
Feldman, T., et al.: Percutaneous mitral repair with the mitraclip system safety and midterm durability in the initial EVEREST (Endovascular Valve Edge-to-Edge REpair Study) cohort. J. Am. Coll. Cardiol. 54(6), 686–694 (2009)
Mansi, T., Voigt, I., Mengue, E.A., Ionasec, R., Georgescu, B., Noack, T., Seeburger, J., Comaniciu, D.: Towards patient-specific finite-element simulation of mitralClip procedure. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 452–459. Springer, Heidelberg (2011)
Lang, R., et al.: EAE/ASE recommendations for image acquisition and display using three-dimensional echocardiography. EHJ Cardiovascular Imaging 13(1) (2012)
Veronesi, F., Corsi, C., Caiani, E., Sugeng, L., Weinert, L., Mor-Avi, V., Lang, R., Lamberti, C.: Semi-automatic tracking for mitral annulus dynamic analysis using real-time 3D echocardiography. In: Computers in Cardiology, pp. 113–116 (2006)
Schneider, R.J., Tenenholtz, N.A., Perrin, D.P., Marx, G.R., del Nido, P.J., Howe, R.D.: Patient-specific mitral leaflet segmentation from 4D ultrasound. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 520–527. Springer, Heidelberg (2011)
Ionasec, R.I., Voigt, I., Georgescu, B., Wang, Y., Houle, H., Vega-Higuera, F., Navab, N., Comaniciu, D.: Patient-specific modeling and quantification of the aortic and mitral valves from 4D cardiac CT and TEE. TMI, 1636–1651 (2010)
Grady, L., Datta, S., Kutter, O., Duong, C., Wein, W., Little, S.H., Igo, S.R., Liu, S., Vannan, M.: Regurgitation quantification using 3D PISA in volume echocardiography. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 512–519. Springer, Heidelberg (2011)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering. In: ICCV (2005)
Hammer, P., Vasilyev, N., Perrin, D., Del Nido, P., Howe, R.: Fast image-based model of mitral valve closure for surgical planning. In: MICCAI, pp. 15–26 (2008)
Thavendiranathan, P., et al.: Automated quantification of mitral inflow and aortic outflow stroke volumes by three-dimensional real-time volume color-flow Doppler transthoracic echocardiography: Comparison with pulsed-wave doppler and cardiac magnetic resonance imaging. J. Am. Soc. Echocardiogr. 25(1), 56–65 (2012)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-d cardiac ct volumes using marginal space learning and steerable features. TMI 27(11), 1668–1681 (2008)
Tu, Z., Zhou, X., Barbu, A., Bogoni, L., Comaniciu, D.: Probabilistic 3D polyp detection in ct images: The role of sample alignment. In: CVPR, pp. 1544–1551 (2006)
Bookstein, F.L.: Principal warps: Thin-plate splines and the decomposition of deformations. PAMI 11(6), 567–585 (1989)
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Wang, Y. et al. (2013). Automatic Detection and Quantification of Mitral Regurgitation on TTE with Application to Assist Mitral Clip Planning and Evaluation. In: Drechsler, K., et al. Clinical Image-Based Procedures. From Planning to Intervention. CLIP 2012. Lecture Notes in Computer Science, vol 7761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38079-2_5
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DOI: https://doi.org/10.1007/978-3-642-38079-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38078-5
Online ISBN: 978-3-642-38079-2
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