Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the periphery of the ${\textit k}$ -space data are sparsely sampled so that neighbouring frames can be merged to construct one temporal frame. However, this view-sharing scheme fundamentally limits the temporal resolution, and it is not possible to change the view-sharing number to achieve different spatio-temporal resolution trade-offs. Although many deep learning approaches have been recently proposed for MR reconstruction from sparse samples, the existing approaches usually require matched fully sampled ${\textit k}$ -space reference data for supervised training, which is not suitable for tMRA due to the lack of high spatio-temporal resolution ground-truth images. To address this problem, here we propose a novel unpaired training scheme for deep learning using optimal transport driven cycle-consistent generative adversarial network (cycleGAN). In contrast to the conventional cycleGAN with two pairs of generator and discriminator, the new architecture requires just a single pair of generator and discriminator, which makes the training much simpler but still improves the performance. Reconstruction results using in vivo tMRA and simulation data set confirm that the proposed method can immediately generate high quality reconstruction results at various choices of view-sharing numbers, allowing us to exploit better trade-off between spatial and temporal resolution in time-resolved MR angiography.

中文翻译：

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深度学习tMRA的不成对训练，以实现灵活的时空分辨率。

时间分辨MR血管造影（tMRA）由于其高度加速的采集而已广泛用于动态对比增强MRI（DCE-MRI）。在tMRA中， $ {\ textit k} $ 对稀疏空间数据进行采样，以便可以合并相邻帧以构造一个时间帧。但是，此视图共享方案从根本上限制了时间分辨率，并且无法更改视图共享数量以实现不同的时空分辨率折衷。尽管最近提出了许多用于从稀疏样本进行MR重建的深度学习方法，但是现有方法通常需要匹配的完全采样 $ {\ textit k} $ 监督训练的空间参考数据，由于缺少高时空分辨率的地面真实图像，因此不适合tMRA。为了解决这个问题，在这里我们提出了一种新的不成对训练方案，该方案使用最佳运输驱动的周期一致的生成对抗网络（cycleGAN）进行深度学习。与具有两对生成器和鉴频器的常规cycleGAN相比，新架构仅需要一对生成器和鉴频器，这使训练变得更加简单，但仍提高了性能。使用体内tMRA和模拟数据集的重建结果证实，该方法可以立即以各种选择的视图共享数生成高质量的重建结果，