RT Journal Article
T1 HDL: 												Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A1 Xing, Xiaodan
A1 Ser, Javier Del
A1 Wu, Yinzhe
A1 Li, Yang
A1 Xia, Jun
A1 Xu, Lei
A1 Firmin, David
A1 Gatehouse, Peter
A1 Yang, Guang
AB 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 investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.
SN 2168-2194
YR 2023
FD 2023-10-01
LK https://hdl.handle.net/11556/4941
UL https://hdl.handle.net/11556/4941
LA eng
NO Xing , 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
NO Publisher Copyright: © 2013 IEEE.
DS TECNALIA Publications
RD 29 sept 2024