  Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the
microstructural arrangement of cardiomyocytes within the myocardium in vivo and
non-invasively, which no other imaging modality allows. This innovative
technology could revolutionise the ability to perform cardiac clinical
diagnosis, risk stratification, prognosis and therapy follow-up. However,
DT-CMR is currently inefficient with over six minutes needed to acquire a
single 2D static image. Therefore, DT-CMR is currently confined to research but
not used clinically. We propose to reduce the number of repetitions needed to
produce DT-CMR datasets and subsequently de-noise them, decreasing the
acquisition time by a linear factor while maintaining acceptable image quality.
Our proposed approach, based on Generative Adversarial Networks, Vision
Transformers, and Ensemble Learning, performs significantly and considerably
better than previous proposed approaches, bringing single breath-hold DT-CMR
closer to reality.
