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Myocardial Function Imaging in Echocardiography Using Deep Learning

Abstract

Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been questioned, and a large inter-vendor variability has been demonstrated. In this work, we propose a novel deep learning based framework for motion estimation in echocardiography, and use this to fully automate myocardial function imaging. A motion estimator was developed based on a PWC-Net architecture, which achieved an average end point error of (0.06±0.04) mm per frame using simulated data from an open access database, on par or better compared to previously reported state of the art. We further demonstrate unique adaptability to image artifacts such as signal dropouts, made possible using trained models that incorporate relevant image augmentations. Further, a fully automatic pipeline consisting of cardiac view classification, event detection, myocardial segmentation and motion estimation was developed and used to estimate left ventricular longitudinal strain in vivo. The method showed promise by achieving a mean deviation of (-0.7±1.6)% compared to a semi-automatic commercial solution for N=30 patients with relevant disease, within the expected limits of agreement. We thus believe that learning-based motion estimation can facilitate extended use of strain imaging in clinical practice.
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Category

Academic article

Language

English

Author(s)

  • Andreas Østvik
  • Ivar Mjåland Salte
  • Erik Smistad
  • Thuy Mi Nguyen
  • Daniela Melichova
  • Harald Brunvand
  • Kristina Haugaa
  • Thor Edvardsen
  • Bjørnar Grenne
  • Lasse Løvstakken

Affiliation

  • SINTEF Digital / Health Research
  • University of Oslo
  • St. Olavs Hospital, Trondheim University Hospital
  • Norwegian University of Science and Technology
  • Sørlandet Hospital Trust
  • Oslo University Hospital

Year

2021

Published in

IEEE Transactions on Medical Imaging

ISSN

0278-0062

Volume

40

Issue

5

Page(s)

1340 - 1351

View this publication at Norwegian Research Information Repository