[논문]Unpaired Deep Learning for Accelerated MRI Using Optimal Transport Driven CycleGAN

Oh, Gyutaek; Sim, Byeongsu; Chung, HyungJin; Sunwoo, Leonard; Ye, Jong Chul

doi:10.1109/tci.2020.3018562

[해외논문] Unpaired Deep Learning for Accelerated MRI Using Optimal Transport Driven CycleGAN 원문보기

IEEE transactions on computational imaging, v.6, 2020년, pp.1285 - 1296

Oh, Gyutaek (Korea Advanced Institute of Science, and Technology (KAIST), Department of Bio, and Brain Engineering, Daejeon, South Korea) , Sim, Byeongsu (Korea Advanced Institute of Science, and Technology (KAIST), Department of Mathematical Sciences, Daejeon, South Korea) , Chung, HyungJin (Korea Advanced Institute of Science, and Technology (KAIST), Department of Bio, and Brain Engineering, Daejeon, South Korea) , Sunwoo, Leonard (Seoul National University Bundang Hospital, Department of Radiology, Seoul National University College of Medicine, Seongnam, South Korea) , Ye, Jong Chul (Korea Advanced Institute of Science, and Technology (KAIST), Department of Bio, and Brain Engineering, Daejeon, South Korea)

Abstract ▼ AI-Helper

Recently, deep learning approaches for accelerated MRI have been extensively studied thanks to their high performance reconstruction in spite of significantly reduced run-time complexity. These neural networks are usually trained in a supervised manner, so matched pairs of subsampled, and fully sampled $k$-space data are required. Unfortunately, it is often difficult to acquire matched fully sampled $k$-space data, since the acquisition of fully sampled $k$-space data requires long scan time, and often leads to the change of the acquisition protocol. Therefore, unpaired deep learning without matched label data has become a very important research topic. In this article, we propose an unpaired deep learning approach using a optimal transport driven cycle-consistent generative adversarial network (OT-cycleGAN) that employs a single pair of generator, and discriminator. The proposed OT-cycleGAN architecture is rigorously derived from a dual formulation of the optimal transport formulation using a specially designed penalized least squares cost. The experimental results show that our method can reconstruct high resolution MR images from accelerated $k$-space data from both single, and multiple coil acquisition, without requiring matched reference data.

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