CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis.

Journal: Journal of magnetic resonance (San Diego, Calif. : 1997)
PMID: 38039654

Abstract

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Authors

  • Xiaodie Chen
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
  • Jiayu Li
    School of Tourism and Geography, School of Biology and Agriculture, Shaoguan University, Shaoguan, China.
  • Dicheng Chen
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, P.O. Box 979, Xiamen, 361005, P.R. China.
  • Yirong Zhou
  • Zhangren Tu
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
  • Meijin Lin
    Department of Applied Marine Physics & Engineering, Xiamen University, Xiamen, China.
  • Taishan Kang
    Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China.
  • Jianzhong Lin
    Magnetic Resonance Center, Zhongshan Hospital Xiamen University, Xiamen 361004, China.
  • Tao Gong
    Educational Testing Service, Princeton, NJ, USA.
  • Liuhong Zhu
    Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
  • Jianjun Zhou
    Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University Xinjiekouwai Street No. 19 Beijing 100875 P. R. China hhuo@bnu.edu.cn.
  • Ou-Yang Lin
    Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Xiamen, China.
  • Jiefeng Guo
    Department of Microelectronics and Integrated Circuit, Xiamen University, Xiamen, China.
  • Jiyang Dong
    Department of Electronic Science, Xiamen University, Xiamen, China.
  • Di Guo
  • Xiaobo Qu

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