Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-03-009 ISMRM Abstract

CloudBrain-VisualAI: A Web Visualized Deep Learning Programming for Cloud Magnetic Resonance Image and Spectroscopy Analysis

Primary: Analysis Methods - Software Tools
Secondary: Analysis Methods - Classification and Prediction
469-03-009 · Software and Analysis for MR Spectroscopy and Chemical Shift Imaging · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row J
Keywords: MRI Segmentation Cloud computing MRS classification Visual programming
Accepted
Yirong Zhou 1, Yanghuang Wu1, Hao Gong1, Jianshu Chen1, Dicheng Chen1, Tao Gong2, Mengtian Lu3, Lianxin Xie4, Ji Qi5, Zhiguo Huang5, Ruibin Ma6, Qin Xu6, Fan Yang7, Di Guo8, Xiaobo Qu1
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
2Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250022, China
3Department of Radiology, Xianning Central Hospital (The First Affiliated Hospital of Hubei University of Science and Technology), Xianning, China
4Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Fujian Province Key Clinical Specialty Construction Project (Medical Imaging Department), Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, China
5China Mobile (Suzhou) Software Technology Company Limited, Suzhou 215004, China
6The Neusoft Medical Technology Co., Ltd, Shanghai 200003, China
7School of Informatics, Xiamen University, Xiamen, China
8School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
Presenting Author: Yirong Zhou

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References

1. Y. Zhou et al., "CloudBrain-ReconAI: A cloud computing platform for MRI reconstruction and radiologists' image quality evaluation," IEEE Transactions on Cloud Computing, vol. 12, no. 4, pp. 1359-1371, 2024.
2. X. Chen et al., "CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis," Journal of Magnetic Resonance, vol. 358, p. 107601, 2024.
3. C. Wang et al., "CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction," arXiv preprint, arXiv:2309.10836, 2023.
4. C. Wang et al., "CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI," Scientific Data, vol. 11, no. 1, p. 687, 2024.
5. J. Lyu et al., "The state-of-the-art in cardiac mri reconstruction: Results of the CMRxRecon challenge in MICCAI 2023," Medical Image Analysis, vol. 101, p. 103485, 2025.
6. Z. Zhang, Q. Liu, and Y. Wang, "Road extraction by deep residual u-net," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 749-753, 2018.
7. R. C. Petersen, "Mild cognitive impairment," Continuum: Lifelong Learning in Neurology, vol. 22, no. 2, pp. 404-418, 2016.
8. C. Wu et al., "Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks," Quantitative Imaging in Medicine and Surgery, vol. 8, no. 10, p. 992, 2018.
9. J. Wen et al., "Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation," Medical Image Analysis, vol. 63, p. 101694, 2020.
10. Q. Yang, Z. Wang, K. Guo, C. Cai, and X. Qu, "Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence," IEEE Signal Processing Magazine, vol. 40, no. 2, pp. 129-140, 2023.
11. Y. Zhou et al., "Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence," Magnetic Resonance Letters, vol. 5, no. 1, p. 200138, 2025.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/469-03-009