Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction

TL;DR

This paper introduces a deep learning approach combining GANs, Vision Transformers, and Ensemble Learning to significantly reduce the acquisition time of diffusion tensor cardiac MRI, making single breath-hold imaging feasible.

Contribution

It presents a novel deep learning framework that decreases DT-CMR scan time by a linear factor while preserving image quality, advancing towards clinical application.

Findings

Outperforms previous methods in image quality and speed

Reduces scan time to enable single breath-hold DT-CMR

Maintains diagnostic image quality with fewer repetitions

Abstract

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.