Learning optimal spatial scales for cardiac strain analysis using a motion atlas

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Abstract

Cardiac motion is inherently tied to the disease state of the heart, and as such can be used to identify the presence and extent of different cardiac pathologies. Abnormal cardiac motion can manifest at different spatial scales of the myocardium depending on the disease present. The importance of spatial scale in the analysis of cardiac motion has not previously been explicitly investigated. In this paper, a novel approach is presented for analysing myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for (1) predicting response to cardiac resynchronisation therapy and (2) identifying the presence of strict left bundle-branch block in a patient cohort of 34. Optimal spatial scales for the two applications were found to be 4% and 16% of left ventricular volume with accuracies of 84.8±8.4% and 81.3±12.6%, respectively, using a repeated, stratified cross-validation.

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Sinclair, M., Peressutti, D., Puyol-Antón, E., Bai, W., Nordsletten, D., Hadjicharalambous, M., … King, A. P. (2017). Learning optimal spatial scales for cardiac strain analysis using a motion atlas. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10124 LNCS, pp. 57–65). Springer Verlag. https://doi.org/10.1007/978-3-319-52718-5_7

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