Learning optimal spatial scales for cardiac strain analysis using a motion atlas
Sinclair M, Peressutti D, Puyol-Anton E, Bai W, Nordsletten D, Hadjicharalambous M, Kerfoot E, Jackson T, Claridge S, Rinaldi CA, Rueckert D, King AP (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10124 LNCS
Pages Range: 57-65
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Athens, GRC
ISBN: 9783319527178
DOI: 10.1007/978-3-319-52718-5_7
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.
Involved external institutions
King’s College London
United Kingdom (GB)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
United Kingdom (GB)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
How to cite
APA:
Sinclair, M., Peressutti, D., Puyol-Anton, E., Bai, W., Nordsletten, D., Hadjicharalambous, M.,... King, A.P. (2017). Learning optimal spatial scales for cardiac strain analysis using a motion atlas. In Maxime Sermesant, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Kristin McLeod, Alistair Young (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 57-65). Athens, GRC: Springer Verlag.
MLA:
Sinclair, Matthew, et al. "Learning optimal spatial scales for cardiac strain analysis using a motion atlas." Proceedings of the 7th International Workshop on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges, STACOM 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, GRC Ed. Maxime Sermesant, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Kristin McLeod, Alistair Young, Springer Verlag, 2017. 57-65.
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