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HDL: hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis

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Title: HDL: hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis
Authors: Xing, X
Del Ser, J
Wu, Y
Li, Y
Xia, J
Xu, L
Firmin, D
Gatehouse, P
Yang, G
Item Type: Journal Article
Abstract: Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complex acquisition produce make it more urgent to produce synthetic digital twins of this imaging modality. In this study, we propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporally down-sampled magnitude CINE images (six times), our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92). These performance scores indicate that our proposed HDL algorithm can be implemented in real-world digital twins for myocardial velocity mapping data simulation. To the best of our knowledge, this work is the first one in the literature investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.
Date of Acceptance: 8-Mar-2022
URI: http://hdl.handle.net/10044/1/95326
DOI: 10.1109/JBHI.2022.3158897
ISSN: 2168-2194
Publisher: Institute of Electrical and Electronics Engineers
Journal / Book Title: IEEE Journal of Biomedical and Health Informatics
Copyright Statement: © 2022 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.
Sponsor/Funder: British Heart Foundation
Commission of the European Communities
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Medical Research Council (MRC)
Medical Research Council (MRC)
Funder's Grant Number: PG/16/78/32402
952172
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
MR/V023799/1
MC_PC_21013
Keywords: eess.IV
eess.IV
cs.AI
cs.CV
physics.med-ph
Publication Status: Published online
Appears in Collections:Bioengineering
National Heart and Lung Institute
Faculty of Medicine
Faculty of Engineering