Literature DB >> 29112879

Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients.

Matthew Sinclair1, Devis Peressutti2, Esther Puyol-Antón3, Wenjia Bai4, Simone Rivolo3, Jessica Webb3, Simon Claridge3, Thomas Jackson3, David Nordsletten3, Myrianthi Hadjicharalambous3, Eric Kerfoot3, Christopher A Rinaldi3, Daniel Rueckert4, Andrew P King3.   

Abstract

Abnormal cardiac motion can indicate different forms of disease, which can manifest at different spatial scales in the myocardium. Many studies have sought to characterise particular motion abnormalities associated with specific diseases, and to utilise motion information to improve diagnoses. However, the importance of spatial scale in the analysis of cardiac deformation has not been extensively investigated. We build on recent work on the analysis of myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for estimating different cardiac biomarkers. We apply a multi-scale strain analysis to a 43 patient cohort of cardiac resynchronisation therapy (CRT) patients using tagged magnetic resonance imaging data for (1) predicting response to CRT, (2) identifying septal flash, (3) estimating QRS duration, and (4) identifying the presence of ischaemia. A repeated, stratified cross-validation is used to demonstrate the importance of spatial scale in our analysis, revealing different optimal spatial scales for the estimation of different biomarkers.
Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cardiac motion atlas; Cardiac resynchronisation therapy; Multi-scale strain

Mesh:

Substances:

Year:  2017        PMID: 29112879     DOI: 10.1016/j.media.2017.10.004

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  4 in total

1.  Cardiac MRI for Patients with Increased Cardiometabolic Risk.

Authors:  Cynthia Philip; Rebecca Seifried; P Gabriel Peterson; Robert Liotta; Kevin Steel; Marcio S Bittencourt; Edward A Hulten
Journal:  Radiol Cardiothorac Imaging       Date:  2021-04-01

2.  MulViMotion: Shape-Aware 3D Myocardial Motion Tracking From Multi-View Cardiac MRI.

Authors:  Qingjie Meng; Chen Qin; Wenjia Bai; Tianrui Liu; Antonio de Marvao; Declan P O'Regan; Daniel Rueckert
Journal:  IEEE Trans Med Imaging       Date:  2022-08-01       Impact factor: 11.037

Review 3.  Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Authors:  Tim Leiner; Daniel Rueckert; Avan Suinesiaputra; Bettina Baeßler; Reza Nezafat; Ivana Išgum; Alistair A Young
Journal:  J Cardiovasc Magn Reson       Date:  2019-10-07       Impact factor: 5.364

Review 4.  Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

Authors:  Anastasia Fotaki; Esther Puyol-Antón; Amedeo Chiribiri; René Botnar; Kuberan Pushparajah; Claudia Prieto
Journal:  Front Cardiovasc Med       Date:  2022-01-10
  4 in total

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