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

Matthew Sinclair; Devis Peressutti; Esther Puyol-Antón; Wenjia Bai; David Nordsletten; Myrianthi Hadjicharalambous; Eric Kerfoot; Tom Jackson; Simon Claridge; C. Aldo Rinaldi; Daniel Rueckert; Andrew P. King

doi:10.1007/978-3-319-52718-5_7

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

Matthew Sinclair, Devis Peressutti, Esther Puyol-Antón, Wenjia Bai, David Nordsletten, Myrianthi Hadjicharalambous, Eric Kerfoot, Tom Jackson, Simon Claridge, C. Aldo Rinaldi, Daniel Rueckert, Andrew P. King

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart
Subtitle of host publication	Imaging and Modelling Challenges - 7th International Workshop, STACOM 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers
Editors	Maxime Sermesant, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Kristin McLeod, Alistair Young
Publisher	Springer Verlag
Pages	57-65
Number of pages	9
ISBN (Print)	9783319527178
DOIs	https://doi.org/10.1007/978-3-319-52718-5_7
State	Published - 2017
Externally published	Yes
Event	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, Greece Duration: 17 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10124 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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
Country/Territory	Greece
City	Athens
Period	17/10/16 → 21/10/16

Access to Document

10.1007/978-3-319-52718-5_7

Cite this

Sinclair, M., Peressutti, D., Puyol-Antón, E., Bai, W., Nordsletten, D., Hadjicharalambous, M., Kerfoot, E., Jackson, T., Claridge, S., Rinaldi, C. A., Rueckert, D., & King, A. P. (2017). Learning optimal spatial scales for cardiac strain analysis using a motion atlas. In M. Sermesant, T. Mansi, M. Pop, K. Rhode, K. McLeod, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 7th International Workshop, STACOM 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers (pp. 57-65). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10124 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-52718-5_7

Sinclair, Matthew ; Peressutti, Devis ; Puyol-Antón, Esther et al. / Learning optimal spatial scales for cardiac strain analysis using a motion atlas. Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 7th International Workshop, STACOM 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. editor / Maxime Sermesant ; Tommaso Mansi ; Mihaela Pop ; Kawal Rhode ; Kristin McLeod ; Alistair Young. Springer Verlag, 2017. pp. 57-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{aeaf149dc53244c78408e3a683bfdfd9,

title = "Learning optimal spatial scales for cardiac strain analysis using a motion atlas",

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.",

author = "Matthew Sinclair and Devis Peressutti and Esther Puyol-Ant{\'o}n and Wenjia Bai and David Nordsletten and Myrianthi Hadjicharalambous and Eric Kerfoot and Tom Jackson and Simon Claridge and Rinaldi, {C. Aldo} and Daniel Rueckert and King, {Andrew P.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 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 ; Conference date: 17-10-2016 Through 21-10-2016",

year = "2017",

doi = "10.1007/978-3-319-52718-5_7",

language = "English",

isbn = "9783319527178",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "57--65",

editor = "Maxime Sermesant and Tommaso Mansi and Mihaela Pop and Kawal Rhode and Kristin McLeod and Alistair Young",

booktitle = "Statistical Atlases and Computational Models of the Heart",

}

Sinclair, M, Peressutti, D, Puyol-Antón, E, Bai, W, Nordsletten, D, Hadjicharalambous, M, Kerfoot, E, Jackson, T, Claridge, S, Rinaldi, CA, Rueckert, D & King, AP 2017, Learning optimal spatial scales for cardiac strain analysis using a motion atlas. in M Sermesant, T Mansi, M Pop, K Rhode, K McLeod & A Young (eds), Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 7th International Workshop, STACOM 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10124 LNCS, Springer Verlag, pp. 57-65, 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, Greece, 17/10/16. https://doi.org/10.1007/978-3-319-52718-5_7

Learning optimal spatial scales for cardiac strain analysis using a motion atlas. / Sinclair, Matthew; Peressutti, Devis; Puyol-Antón, Esther et al.
Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 7th International Workshop, STACOM 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. ed. / Maxime Sermesant; Tommaso Mansi; Mihaela Pop; Kawal Rhode; Kristin McLeod; Alistair Young. Springer Verlag, 2017. p. 57-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10124 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning optimal spatial scales for cardiac strain analysis using a motion atlas

AU - Sinclair, Matthew

AU - Peressutti, Devis

AU - Puyol-Antón, Esther

AU - Bai, Wenjia

AU - Nordsletten, David

AU - Hadjicharalambous, Myrianthi

AU - Kerfoot, Eric

AU - Jackson, Tom

AU - Claridge, Simon

AU - Rinaldi, C. Aldo

AU - Rueckert, Daniel

AU - King, Andrew P.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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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DO - 10.1007/978-3-319-52718-5_7

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AN - SCOPUS:85011411912

SN - 9783319527178

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 57

EP - 65

BT - Statistical Atlases and Computational Models of the Heart

A2 - Sermesant, Maxime

A2 - Mansi, Tommaso

A2 - Pop, Mihaela

A2 - Rhode, Kawal

A2 - McLeod, Kristin

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PB - Springer Verlag

T2 - 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

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ER -

Sinclair M, Peressutti D, Puyol-Antón E, Bai W, Nordsletten D, Hadjicharalambous M et al. Learning optimal spatial scales for cardiac strain analysis using a motion atlas. In Sermesant M, Mansi T, Pop M, Rhode K, McLeod K, Young A, editors, Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 7th International Workshop, STACOM 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. Springer Verlag. 2017. p. 57-65. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-52718-5_7

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

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