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
Conference paper

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

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)
Cite this paper as:
Sinclair M. et al. (2017) Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas. In: Mansi T., McLeod K., Pop M., Rhode K., Sermesant M., Young A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science, vol 10124. Springer, Cham

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 \(84.8 \pm 8.4\%\) and \(81.3 \pm 12.6\%\), respectively, using a repeated, stratified cross-validation.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Matthew Sinclair
    • 1
  • Devis Peressutti
    • 1
  • Esther Puyol-Antón
    • 1
  • Wenjia Bai
    • 2
  • David Nordsletten
    • 1
  • Myrianthi Hadjicharalambous
    • 1
  • Eric Kerfoot
    • 1
  • Tom Jackson
    • 1
  • Simon Claridge
    • 1
  • C. Aldo Rinaldi
    • 1
  • Daniel Rueckert
    • 2
  • Andrew P. King
    • 1
  1. 1.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  2. 2.Biomedical Image Analysis GroupImperial College LondonLondonUK

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