TY - CHAP
T1 - Learning optimal spatial scales for cardiac strain analysis using a motion atlas
AU - Sinclair, Matthew
AU - Peressutti, Devis
AU - Puyol Anton, Esther
AU - Bai, Wenjia
AU - Nordsletten, David
AU - Hadjicharalambous, Myrianthi
AU - Kerfoot, Eric
AU - Jackson, Tom
AU - Claridge, Simon
AU - Rinaldi, Christopher Aldo
AU - Rueckert, Daniel
AU - King, Andrew
PY - 2017/1/24
Y1 - 2017/1/24
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.
UR - http://www.scopus.com/inward/record.url?scp=85011411912&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-52718-5_7
DO - 10.1007/978-3-319-52718-5_7
M3 - Conference paper
AN - SCOPUS:85011411912
SN - 9783319527178
VL - 10124 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 65
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer‐Verlag Berlin Heidelberg
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
Y2 - 17 October 2016 through 21 October 2016
ER -