XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1.
5 papers.
- 2021One-dimensional Deep Low-rank and Sparse Network for Accelerated MRIcited by 27
- 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 15
- 2022A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imagingcited by 5
- 2024Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRIcited by 4
- 2023Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligencecited by 2
- 2024Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI24 Feb 2024
- 2023Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence18 Oct 2023
- 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction25 Jul 2023
- 2022A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging23 Oct 2022
- 2021One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI9 Dec 2021