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Fully automated myocardial strain estimation from cine MRI using convolutional neural networks

Research output: Chapter in Book/Report/Conference proceedingConference paper

Esther Puyol-Anton, Bram Ruijsink, Wenjia Bai, Helene Langet, Mathieu De Craene, Julia A. Schnabel, Paolo Piro, Andrew P. King, Matthew Sinclair

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1139-1143
Number of pages5
Volume2018-April
ISBN (Electronic)9781538636367
DOIs
StatePublished - 23 May 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/04/20187/04/2018

Documents

  • Fully automated myocardial strain_PUYOL-ANTON_Published23May2018_GREEN AAM

    Fully_automated_myocardial_strain_PUYOL_ANTON_Published23May2018_GREEN_AAM.pdf, 700 KB, application/pdf

    8/08/2018

    Accepted author manuscript

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

King's Authors

Abstract

Cardiovascular magnetic resonance myocardial feature tracking (CMR-FT) is a promising method for quantification of cardiac function from standard steady-state free precession (SSFP) images. However, currently available techniques require operator dependent and time-consuming manual intervention, limiting reproducibility and clinical use. In this paper, we propose a fully automated pipeline to compute left ventricular (LV) longitudinal and radial strain from 2- and 4-chamber cine acquisitions, and LV circumferential and radial strain from the short-axis imaging. The method employs a convolutional neural network to automatically segment the myocardium, followed by feature tracking and strain estimation. Experiments are performed using 40 healthy volunteers and 40 ischemic patients from the UK Biobank dataset. Results show that our method obtained strain values that were in excellent agreement with the commercially available clinical CMR-FT software CVI42 (Circle Cardiovascular Imaging, Calgary, Canada).

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