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.