Accelerating DT-CMR with Deep Learning-based Tensor De-noising and Breath Hold Reduction

Motivation: DT-CMR can revolutionise diagnosis and treatment of heart conditions by non-invasively imaging cardiomyocyte microstructure, but currently long acquisition times prevent clinical use. Goal(s): Reduce the number of breath-holds required for in-vivo DT-CMR acquisitions, resulting in significantly reduced scan times with minimal image quality loss. Approach: We developed a deep learning model based on Generative Adversarial Networks, Vision Transformers, and Ensemble Learning to de-noise diffusion tensors computed from reduced-repetition DT-CMR data. We compared model performance to conventional linear fitting methods and a baseline deep learning approach. Results: Our model reduced noise over 20% compared to previous state-of-the-art approaches while retaining known clinically-relevant myocardial properties. Impact: This breakthrough in DT-CMR acquisition efficiency could enable rapid microstructural phenotyping of the myocardium in the clinic for the first time, revolutionising personalised diagnosis and treatment by unlocking DT-CMR’s ability to non-invasively characterise heart muscle organisation at the cellular level.