Abstract #4302

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

Synopsis

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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