XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI Reconstruction
Yirong Zhou1, Chen Qian1, Yi Guo2, Zi Wang1, Jian Wang1, Biao Qu3, Di Guo2, Yongfu You4, and Xiaobo Qu1
1Biomedical Intelligent Cloud R&D Center, Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, 2School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, 3Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China, 4Biomedical Intelligent Cloud R&D Center, School of Electronic Science and Engineering, China Mobile Group, Xiamen, China
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms. In this work, we develop a high-performance medical intelligence cloud computing platform (XCloud-pFISTA) to reconstruct MRI images from undersampled k-space data. Two state-of-the-art approaches of the Projected Fast Iterative Soft-Thresholding Algorithm (pFISTA) family have been successfully implemented on the cloud. This work can be considered as a good example of cloud-based medical image reconstruction and may benefit the future development of integrated reconstruction and online diagnosis system.
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