Abstract
Abnormal myocardial motion occurs in many cardiac pathologies, though in different ways, depending on the disease, some of which can result in negative clinical outcomes. Therefore, a better understanding of the contractile capability of the tissue is crucial in providing an improved and patient-specific clinical outcome [4]. Cardiovascular Magnetic Resonance Imaging (CMR) is considered the gold standard for the assessment of cardiac function and has the potential to also be used for routine tissue strain analysis because of its high availability in clinical practice. In this study we estimate the local strain in myocardial tissue over a cardiac cycle using cine MRI imaging to perform the analysis. To quantify the tissue displacement, we use the diffeomorphic demons registration algorithm [15] in a multi-step 3D registration, for the minimization of cumulative errors propagation. Using the displacement gradient of the deformation, individual voxel strain curves are computed. We present a novel method for parcellating the myocardium into regions based on the strain behaviour of clusters of voxels. We define the supervoxels using the Simple Linear Iterative Clustering (SLIC) algorithm [1] inside a predefined mask. The results are consistent with late gadolinium enhancement scar identification.
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References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2281 (2012)
Axel, L., Montillo, A., Kim, D.: Tagged magnetic resonance imaging of the heart a survey. Med. Image Anal. 9, 376–393 (2005)
Bai, W., Peressutti, D., Parisot, S., Oktay, O., Rajchl, M., O’Regan, D., Cook, S., King, A., Rueckert, D.: Beyond the AHA 17-segment model: motion-driven parcellation of the left ventricle. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2015. LNCS, vol. 9534, pp. 13–20. Springer, Heidelberg (2016). doi:10.1007/978-3-319-28712-6_2
Dall’Armellina, E., Choudhury, R.P.: The role of cardiovascular magnetic resonance in patients with acute coronary syndromes. Progr. Cardiovasc. Dis. 54(3), 230–239 (2011)
Duchateau, N., Sermesant, M.: Prediction of infarct localization from myocardial deformation. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2015. LNCS, vol. 9534, pp. 51–59. Springer, Heidelberg (2016). doi:10.1007/978-3-319-28712-6_6
Irving, B.: SLIC in a defined mask with applications to medical imaging, pp. 1–5 (2016). http://arxiv.org/abs/1606.09518
Irving, B., Franklin, J.M., Papiez, B.W., Anderson, E.M., Sharma, R.A., Gleeson, F.V., Brady, S.M., Schnabel, J.A.: Pieces-of-parts for supervoxel segmentation with global context: application to DCE-MRI tumour delineation. Med. Image Anal. 32, 69–83 (2016)
Irving, B.J., Goussard, P., Andronikou, S., Gie, R., Douglas, T.S., Todd-Pokropek, A., Taylor, P.: Computer assisted detection of abnormal airway variation in CT scans related to paediatric tuberculosis. Med. Image Anal. 18(7), 963–976 (2014)
Kroon, D.J., Slump, C.H.: MRI modalitiy transformation in demon registration. In: Proceedings of the Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 963–966. IEEE Press, Piscataway (2009)
Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: ILogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. Int. J. Comput. Vis. 92(1), 92–111 (2011)
Mansi, T., Peyrat, J.-M., Sermesant, M., Delingette, H., Blanc, J., Boudjemline, Y., Ayache, N.: Physically-constrained diffeomorphic demons for the estimation of 3D myocardium strain from cine-MRI. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 201–210. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01932-6_22
Peressutti, D., Bai, W., Shi, W., Tobon-Gomez, C., Jackson, T., Sohal, M., Rinaldi, A., Rueckert, D., King, A.: Towards left ventricular scar localisation using local motion descriptors. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2015. LNCS, vol. 9534, pp. 30–39. Springer, Heidelberg (2016). doi:10.1007/978-3-319-28712-6_4
Spottiswoode, B.S., Zhong, X., Hess, A.T., Kramer, C.M., Meintjes, E.M., Mayosi, B.M., Epstein, F.H.: Tracking myocardial motion from cine DENSE images using spatiotemporal phase unwrapping and temporal fitting. IEEE Trans. Med. Imaging 26(1), 15–30 (2007)
Tobon-Gomez, C., et al.: Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med. Image Anal. 17(6), 632–648 (2013)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(Suppl. 1), S61–S72 (2009)
Acknowledgements
This research is supported by the RCUK Digital Economy Programme grant number EP/G036861/1, Oxford Centre for Doctoral Training in Healthcare Innovation. ED acknowledges the BHF intermediate clinical research fellow grant (FS/13/71/30378) and the NIHR BRC. VG is supported by a BBSRC grant (BB/I012117/1), an EPSRC grant (EP/J013250/1) and by BHF New Horizon Grant NH/13/30238.
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Popescu, I.A., Irving, B., Borlotti, A., Dall’Armellina, E., Grau, V. (2017). Myocardial Scar Quantification Using SLIC Supervoxels - Parcellation Based on Tissue Characteristic Strains. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science(), vol 10124. Springer, Cham. https://doi.org/10.1007/978-3-319-52718-5_20
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DOI: https://doi.org/10.1007/978-3-319-52718-5_20
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