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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer‐Verlag Berlin Heidelberg
Pages57-65
Number of pages9
Volume10124 LNCS
ISBN (Print)9783319527178
DOIs
Accepted/In press15 Jul 2016
Published24 Jan 2017
Event7th 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 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10124 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference7th 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/TerritoryGreece
CityAthens
Period17/10/201621/10/2016

Documents

  • Learning Optimal Spatial Scales_SINCLAIR_Publishedonline24January2017_GREEN AAM

    stacom_final.pdf, 5.4 MB, application/pdf

    Uploaded date:16 May 2017

    Version:Accepted author manuscript

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King's Authors

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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