HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis

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dc.contributor.authorXing, Xiaodan
dc.contributor.authorSer, Javier Del
dc.contributor.authorWu, Yinzhe
dc.contributor.authorLi, Yang
dc.contributor.authorXia, Jun
dc.contributor.authorXu, Lei
dc.contributor.authorFirmin, David
dc.contributor.authorGatehouse, Peter
dc.contributor.authorYang, Guang
dc.contributor.institutionIA
dc.date.accessioned2024-09-06T10:55:01Z
dc.date.available2024-09-06T10:55:01Z
dc.date.issued2023-10-01
dc.descriptionPublisher Copyright: © 2013 IEEE.
dc.description.abstractSynthetic 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 investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.en
dc.description.statusPeer reviewed
dc.format.extent9
dc.identifier.citationXing , X , Ser , J D , Wu , Y , Li , Y , Xia , J , Xu , L , Firmin , D , Gatehouse , P & Yang , G 2023 , ' HDL : Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis ' , IEEE Journal of Biomedical and Health Informatics , vol. 27 , no. 10 , pp. 5134-5142 . https://doi.org/10.1109/JBHI.2022.3158897
dc.identifier.doi10.1109/JBHI.2022.3158897
dc.identifier.issn2168-2194
dc.identifier.urihttps://hdl.handle.net/11556/4941
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85126540214&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Journal of Biomedical and Health Informatics
dc.relation.projectIDHorizon 2020 Framework Programme, H2020, 952172
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsCINE MRI
dc.subject.keywordsCardiac imaging
dc.subject.keywordsdigital twins
dc.subject.keywordsimage synthesis
dc.subject.keywordsmyocardial velocity mapping
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsHealth Informatics
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsHealth Information Management
dc.titleHDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysisen
dc.typejournal article
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