Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation
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Published version
Author(s)
Type
Journal Article
Abstract
Purpose In this work we develop and validate a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT‐CMR) data powered by deep learning. Methods A U‐Net based convolutional neural network was developed and trained to segment the heart in short‐axis DT‐CMR images. This was used as the basis to automate and enhance several stages of the DT‐CMR tensor calculation workflow, including image registration and removal of data corrupted with artifacts, and to segment the left ventricle. Previously collected and analyzed scans (348 healthy scans and 144 cardiomyopathy patient scans) were used to train and validate the U‐Net. All data were acquired at 3 T with a STEAM‐EPI sequence. The DT‐CMR postprocessing and U‐Net training/testing were performed with MATLAB and Python TensorFlow, respectively. Results The U‐Net achieved a median Dice coefficient of 0.93 [0.92, 0.94] for the segmentation of the left‐ventricular myocardial region. The image registration of diffusion images improved with the U‐Net segmentation (P < .0001), and the identification of corrupted images achieved an F1 score of 0.70 when compared with an experienced user. Finally, the resulting tensor measures showed good agreement between an experienced user and the fully automated method. Conclusion The trained U‐Net successfully automated the DT‐CMR postprocessing, supporting real‐time results and reducing human workload. The automatic segmentation of the heart improved image registration, resulting in improvements of the calculated DT parameters.
Date Issued
2020-11-01
Date Acceptance
2020-04-01
ISSN
0740-3194
Publisher
Wiley
Start Page
2801
End Page
2814
Journal / Book Title
Magnetic Resonance in Medicine
Volume
84
Issue
5
Copyright Statement
© 2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine
This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Sponsor
British Heart Foundation
British Heart Foundation
Identifier
https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.28294
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000527923600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
RG/19/1/34160
RG/19/1/34160
Subjects
Science & Technology
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
cardiac
deep learning
diffusion tensor imaging
image processing
machine learning
SPIN-ECHO
HEART
cardiac
deep learning
diffusion tensor imaging
image processing
machine learning
Science & Technology
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
cardiac
deep learning
diffusion tensor imaging
image processing
machine learning
SPIN-ECHO
HEART
Nuclear Medicine & Medical Imaging
0903 Biomedical Engineering
Publication Status
Published
Date Publish Online
2020-04-23