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Time-resolved MR angiography (tMRA) has been widely used for dynamic contrastenhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, theperiphery of the k-space data are sparsely sampled so that neighbouring framescan be merged to construct one temporal frame. However, this view-sharingscheme fundamentally limits the temporal resolution, and it is not possible tochange the view-sharing number to achieve different spatio-temporal resolutiontrade-off. Although many deep learning approaches have been recently proposedfor MR reconstruction from sparse samples, the existing approaches usuallyrequire matched fully sampled k-space reference data for supervised training,which is not suitable for tMRA. This is because high spatio-temporal resolutionground-truth images are not available for tMRA. To address this problem, herewe propose a novel unsupervised deep learning using optimal transport drivencycle-consistent generative adversarial network (cycleGAN). In contrast to theconventional cycleGAN with two pairs of generator and discriminator, the newarchitecture requires just a single pair of generator and discriminator, whichmakes the training much simpler and improves the performance. Reconstructionresults using in vivo tMRA data set confirm that the proposed method canimmediately generate high quality reconstruction results at various choices ofview-sharing numbers, allowing us to exploit better trade-off between spatialand temporal resolution in time-resolved MR angiography.

Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolution