Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution

• Published in: IEEE transactions on medical imaging Vol. 40; no. 1; pp. 166 - 179
• Main Authors: Cha, Eunju, Chung, Hyungjin, Kim, Eung Yeop, Ye, Jong Chul
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angiography; cycleGAN; Deep learning; dynamic contrast enhanced MRI; Generators; Image reconstruction; In vivo methods and tests; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; optimal transport; penalized least squares (PLS); Reconstruction; Spatial resolution; Temporal resolution; Time-resolved MRA; Tradeoffs; Training
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2020.3023620

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 <inline-formula> <tex-math notation="LaTeX">{\textit k} </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">{\textit k} </tex-math></inline-formula>-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.