Abstract
Cardiac fibre architecture plays a key role in heart function. Recently, the estimation of fibre structure has been simplified with diffusion tensor MRI (DT-MRI). In order to assess the heart architecture and its underlying function, with the goal of dealing with pathological tissues and easing inter-patient comparisons, we propose a methodology for finding cardiac myofibrille trace correspondences across a fibre population obtained from DT-MRI data. It relies on the comparison of geometrical and topological clustering operating on different fibre representation modes (fixed length sequences of 3-D coordinates with or without ordering strategy, and 9-D vectors for trace shape approximation). In geometrical clustering (or k-means) each fibre path is assigned to the cluster with nearest barycenter. In topological (or spectral) clustering the data is represented by a similarity graph and the graph vertices are divided into groups so that intra-cluster connectivity is maximized and inter-cluster connectivity is minimized.
Using these different clustering methods and fibre representation modes, we predict different fibre trace classifications for the same cardiac dataset. These classification results are compared to the human heart architecture models proposed in the literature.
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References
Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: SODA (2007)
Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine 44(4), 625–632 (2000)
Le Bihan, D., Van Zijl, P.: From the diffusion coefficient to the diffusion tensor. NMR Biomed. 15, 431–434 (2002)
Brun, A., Knutsson, H., Park, H.J., Shenton, M.E., Westin, C.F.: Clustering fiber traces using normalized cuts. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 368–375. Springer, Heidelberg (2004)
Corouge, I., Gouttard, S., Gerig, G.: Towards a shape model of white matter fiber bundles using diffusion tensor MRI. In: ISBI, pp. 344–347 (2004)
Ding, Z., Gore, J.C., Anderson, A.W.: Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. MRM 49, 716–721 (2003)
Frindel, C., Robini, M., Rapacchi, S., Stephant, E., Zhu, Y., Croisille, P.: Towards in vivo diffusion tensor MRI on human heart using edge-preserving regularization. In: Int. Conf. IEEE EMBS 2007, pp. 6007–6010 (2007)
Frindel, C., Schaerer, J., Gueth, P., Clarysse, P., Zhu, Y.M., Robini, M.: A global approach to cardiac tractography. In: ISBI, pp. 883–886 (2008)
Gerig, G., Gouttard, S., Corouge, I.: Analysis of brain white matter via fiber tract modeling. In: EMBS, p. 426 (2004)
Gilbert, S.H., Benson, A.P., Li, P.L., Holden, A.V.: Regional localisation of left ventricular sheet structure: integration with current models of cardiac fibre, sheet and band structure. Eur. J. Cardiothorac. Surg. 32, 231–249 (2007)
Harrington, K.B., Rodriguez, F., Cheng, A., Langer, F., Ashikaga, H., Daughters, G.T., Criscione, J.C., Ingels, N.B., Miller, D.C.: Direct measurement of transmural laminar architecture in the anterolateral wall of the ovine left ventricle: new implications for wall thickening mechanics. Am. J. Physiol. Heart Circ. Physiol. 288, H132430 (2005)
Helm, P.A., Younes, L., Beg, M.F., Ennis, D.B., Leclercq, C., Faris, O.P., McVeigh, E., Kass, D., Miller, M.I., Winslow, R.L.: Evidence of structural remodeling in the dyssynchronous failing heart. Circ. Res. 98, 125–132 (2006)
Jonasson, L., Hagmann, P., Thiran, J.-P., Wedeen, V.J.: Fiber tracts of high angular resolution diffusion mri are easily segmented with spectral clustering. In: ISMRM (2005)
Streeter Jr., D.D.: Gross morphology and fiber geometry of the heart, pp. 61–112. Williams and Wilkins, Baltimore (1979)
Kocica, M.J., Corno, A.F., Carreras-Costa, F., Ballester-Rodes, M., Moghbel, M.C., Cueva, C.N.C., Lackovic, V., Kanjuh, V.I., Torrent-Guasp, F.: The helical ventricular myocardial band: global, three-dimensional, functional architecture of the ventricular myocardium. Eur. J. Cardiothorac. Surg. 29, S21–S40 (2006)
Krehl, L.: Beiträge zur Kenntniss der Füllung und Entleerung des Herzens. Abh. Math.-Phys. Kl Saechs. Ges. Wiss. 17, 341–362 (1891)
LeGrice, I.J., Smaill, B.H., Chai, L.Z., Edgar, S.G., Gavin, J.B., Hunter, P.J.: Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am. J. Physiol. 269, H57182 (1995)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inform. Theory 28, 129–137 (1982)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22, 888–905 (2000)
Shimony, J., Snyder, A., Lori, N., Contum, T.: Automated fuzzy clustering of neuronal pathways in diffusion tensor tracking. In: ISMRM (2005)
Tsai, A., Westin, C.F., Hero, A.O., Willsky, A.S.: Fiber tract clustering on manifolds with dual rootedgraphs. In: CVPR, pp. 1–6 (2007)
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Frindel, C., Robini, M., Schaerer, J., Croisille, P., Zhu, YM. (2009). Cardiac Fibre Trace Clustering for the Interpretation of the Human Heart Architecture. 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_5
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DOI: https://doi.org/10.1007/978-3-642-01932-6_5
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