论文标题
更快的扩散心脏MRI，基于深度学习的呼吸保持降低
Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction
论文作者
论文摘要
扩散张量心脏磁共振（DT-CMR）使我们能够探测体内心肌内心肌细胞的微观结构布置，这是没有其他成像方式的。这种创新的技术可以彻底改变执行心脏临床诊断，风险分层，预后和治疗随访的能力。但是，DT-CMR目前效率低下，获得单个2D静态图像所需的六分钟以上。因此，DT-CMR目前仅限于研究，但在临床上不使用。我们建议减少生产DT-CMR数据集并随后将其降低所需的重复数量，从而减少通过线性因子的收购时间，同时保持可接受的图像质量。我们提出的方法基于生成的对抗网络，视觉变压器和合奏学习，其性能明显优于以前提出的方法，使单一的呼吸呼吸型DT-CMR更接近现实。
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.
