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cardiometabolic risk; carotid intima-media thickness; childhood obesity; subclinical atherosclerosis; reduced order model; atrial fibrillation; Circle of Willis variants; cerebral blood flow; sensitivity analysis; cardiac MRI; machine learning; left ventricle segmentation; cardiac function; diffusion tensor imaging; in vivo cDTI; chronic infarction; cardiac microstructure; radial diffusivity; swine infarction model; cardiac image segmentation; reliability and robustness; deep learning; cardiac magnetic resonance imaging; inverse models; data assimilation; cardiovascular imaging; image-based kinematics; biomechanics; tissue mechanics; hemodynamics; patient-specific models; coronary vasculature; lumped parameter model; fractional flow reserve; computational cardiology; compounded echocardiography; volume stitching; 3D registration; mosaicing; 3D TEE; mitral valve; monogenic signal; cardiac imaging; multimodal; electrophysiology; biophysical modelling; inverse problems; parameter optimisation; smoothed particle hydrodynamics; meshless model; cardiac resynchronization therapy; CRT-EPiggy19 challenge; cardiac radiofrequency ablation; 3D-printing; Layfomm-40; physical simulation; simulation training; thermochromic pigments; cardiotoxicity; MRI; fibrosis; chemotherapy; doxorubicin; voltage mapping; arrhythmia; augmentation; cardiac segmentation; domain invariant features; disentangled representation; generative adversarial network; image quality; mutual information; reconstruction; variational autoencoder; n/a