Abstract
We propose a novel framework to predict the location of a myocardial infarct from local wall deformation data. Non-linear dimensionality reduction is used to estimate the Euclidean space of coordinates encoding deformation patterns. The infarct location of a new subject is inferred by two consecutive interpolations, formulated as multiscale kernel regressions. They consist in (i) finding the low-dimensional coordinates associated to the measured deformation pattern, and (ii) estimating the possible infarct location associated to these coordinates. These concepts were tested on a database of 500 synthetic cases generated from a realistic electromechanical model of the two ventricles. The database consisted of infarcts of random extent, shape, and location overlapping the whole left-anterior-descending coronary territory. We demonstrate that our method is accurate and significantly overcomes the limitations of the clinically-used thresholding of the deformation patterns (average area under the ROC curve of 0.992\(\pm \)0.011 vs. 0.812\(\pm \)0.124, p\(<\)0.001).
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Acknowledgements
The authors acknowledge the European Union 7th Framework Programme (VP2HF: FP7-2013-611823) and the European Research Council (MedYMA: ERC-AdG-2011-291080). They also thank their colleagues R. Mollero and S. Giffard-Roisin for their support on practical aspects of the SOFA simulations, and their collaborators M. De Craene (Philips Suresnes, France) and E. Saloux (CHU Caen, France) for discussions on these concepts.
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Duchateau, N., Sermesant, M. (2016). Prediction of Infarct Localization from Myocardial Deformation. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_6
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DOI: https://doi.org/10.1007/978-3-319-28712-6_6
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