Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction
This work addresses a bottleneck in making DT-CMR clinically viable for cardiac diagnosis and monitoring, though it appears incremental as it builds on existing deep learning techniques.
The paper tackles the inefficiency of Diffusion Tensor Cardiac MRI (DT-CMR), which requires over six minutes for a 2D image, by reducing repetitions and denoising to decrease acquisition time linearly while maintaining image quality. The proposed deep learning approach outperforms previous methods, advancing towards single breath-hold DT-CMR.
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.