Abstract
The presence of an infarct scar in the heart generates abnormal electrical pathways that may trigger the occurrence of arrhythmic episodes. While precise models of the electric propagation in the heart have been proposed, we are just starting to observe and analyze infarct scars using high-resolution imaging techniques. Recent observations have shown that the scar is a highly heterogeneous tissue, characterized by a complex interface with surrounding myocardium. For instance, the infarct scar is perforated by tunnels of live tissue, which could generate abnormal activation pathways and therefore facilitate arrhythmia episodes. In order to characterize the role of such structures, we need to first delineate them. In this paper, we propose an automatic method for the detection of these tunnels of normal tissue through scars in high resolution MR images.
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References
Lombardi, F.: Timing of arrhythmic death after myocardial infarction: does it affect timing of ICD implantation? Eur. Heart Journal 26(14), 1350–1352 (2005)
Kim, R., Fieno, D., Parrish, T., Harris, K., Chen, E.L., Simonetti, O., Bundy, J., Finn, P., Klocke, F., Judd, R.: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100(19), 1992–2002 (1999)
Klocke, F., Wu, E., Lee, D.: “Shades of gray” in cardiac magnetic resonance images of infarcted myocardium. Circulation 114, 8–10 (2006)
Schuleri, K., Centola, M., George, R., Amado, L., Evers, K., Kitagawa, K., Vavere, A., Evers, R., Hare, J., McVeigh, E., Lima, J., Lardo, A.: Characterization of peri-infarct zone heterogeneity by contrast enhanced multi-detector CT: Comparison with MR imaging. J. Am. Coll. Cardiol. (in press)
Yan, A., Shayne, A., Brown, K., Gupta, S., Chan, C., Luu, T., Carli, M.D., Reynolds, H., Stevenson, W., Kwong, R.: Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance imaging is a powerful predictor of post-myocardial infarction mortality. Circulation 114, 32–39 (2006)
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Computing Surveys 36(2), 81–121 (2004)
Mahadevan, V., Narasimha-Iyer, H., Roysam, B., Tanenbaum, H.: Robust model-based vasculature detection in noisy biomedical images. IEEE Trans. Inform. Technol. Biomed. 8(3), 360–376 (2004)
Wörz, S., Rohr, K.: A new 3D parametric intensity model for accurate segmentation and quantification of human vessels. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 491–499. Springer, Heidelberg (2004)
Can, A., Shen, H., Turner, J., Tanenbaum, H., Roysam, B.: Rapid automated tracing and feature extraction from live high-resolution retinal fundus images using direct exploratory algorithms. IEEE Trans. Pattern Anal. Machine Intell. 25(2), 125–138 (1999)
Aylward, S., Weeks, S., Bullitt, E.: Analysis of the parameter space of a metric for registering 3D vascular images. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 932–939. Springer, Heidelberg (2001)
Sato, Y., Araki, T., Hanayama, M., Naito, H., Tamura, S.: A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition. IEEE Trans. on Med. Imag. 17, 121–137 (1998)
Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Jackowski, M., Papademetris, X., Dobrucki, L., Sinusas, A., Staib, L.: Characterizing vascular connectivity from microCT images. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 701–708. Springer, Heidelberg (2005)
Sofka, M., Stewart, C.: Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. Med. Imag. 25(12), 1531–1546 (2006)
Geman, D., Jedynak, B.: An active testing model for tracking roads from satellite images. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1–14 (1996)
Qian, X., Brennan, M., Dione, D., Dubrucki, W., Jackowski, M., Breuer, C., Sinusas, A.J., Papademitris, X.: A non-parametric vessel detection method for complex vascular structures. Med. Imag. Anal. 13, 49–61 (2009)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society 39, 1–38 (1977)
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Vidal, C., Ashikaga, H., McVeigh, E.R. (2009). A Statistical Approach for Detecting Tubular Structures in Myocardial Infarct Scars. In: Ayache, N., Delingette, H., Sermesant, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2009. Lecture Notes in Computer Science, vol 5528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01932-6_13
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DOI: https://doi.org/10.1007/978-3-642-01932-6_13
Publisher Name: Springer, Berlin, Heidelberg
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