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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 an open-access, easy-to-use and 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.
For accelerated multi-dimensional NMR spectroscopy, non-uniform sampling is a powerful approach but requires sophisticated algorithms to reconstruct undersampled data. Here, we first devise a high-performance deep learning framework (MoDern), which s
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for tradition
Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We
Purpose: To develop an improved self-supervised learning strategy that efficiently uses the acquired data for training a physics-guided reconstruction network without a database of fully-sampled data. Methods: Currently self-supervised learning for
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured appro