CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis ( [English] )

陈晓蝶1,#, 李嘉钰1,#, 陈棣成1, 周毅荣1, 涂章仁1, 林美金2, 康泰山3, 林建忠3, 巩涛4, 朱柳红5, 周建军5, 欧阳林6, 郭杰锋7, 董继扬1, 郭迪 8, 屈小波1*

 

1厦门大学，电子科学系，福建等离子体与磁共振重点研究实验室，中国，厦门;

2厦门大学应用海洋物理与工程系，中国，厦门;

3厦门大学附属中山医院磁共振科，中国，厦门;.

4山东第一医科大学附属省立医院放射科，中国，山东济南;

5复旦大学附属中山医院放射科，中国，厦门;

6厦门大学医学院附属东南医院医学影像科，中国，厦门;

7厦门大学微电子与集成电路系，中国，厦门;

8厦门理工学院，计算机与信息工程学院，中国，厦门;

联系人: quxiaobo<|at|>xmu.edu.cn

 

引用:  Xiaodie Chen, Jiayu Li, Dicheng Chen, Yirong Zhou, Zhangren Tu, Meijin Lin, Taishan Kang, Jianzhong Lin, Tao Gong, Liuhong Zhu, Jianjun Zhou, Jiefeng Guo, Jiyang Dong, Di Guo, Xiaobo Qu∗, CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis,Journal of Magnetic Resonance, vol. 358, pp. 107601, 2023.

全文链接：https://www.sciencedirect.com/science/article/pii/S1090780723002367?via%3Dihub

 

摘要:

  磁共振波谱（Magnetic Resonance Spectroscopy, MRS）是一种重要的临床疾病诊断方法，通过MRS可以观察代谢物的信号强度，进一步推断其浓度。虽然磁共振厂商普遍提供谱图可视化和代谢物定量等基本功能，但由于缺乏易用的处理软件或平台，MRS的临床研究推广仍受到限制。为了解决这个问题，我们开发了磁共振波谱定量分析平台CloudBrain-MRS。这是一个基于云计算的在线平台，提供强大的硬件和先进的算法。该平台只需通过浏览器即可访问，用户无需安装任何程序。CloudBrain-MRS 还集成了经典的 LCModel 模型和先进的人工智能算法，并支持对来自不同供应商的 MRS 数据进行批量预处理、量化和分析。此外，该平台还提供以下实用功能：（1）自动统计分析，寻找疾病的生物标记物；（2）经典量化算法与人工智能量化算法之间的一致性验证；（3）彩色三维可视化，方便观察单个代谢物谱。最后，健康受试者和轻度认知障碍患者的数据被用来展示该平台的功能。这是首个支持使用人工智能算法处理活体 MRS 数据的云计算平台，已共享在 MRSHub社区，提供至少两年的免费访问和服务。如果您感兴趣，请访问https://mrshub.org/software_all/#CloudBrain-MRS 或 https://csrc.xmu.edu.cn/CloudBrain.html。

关键词: 磁共振波谱、云计算、量化、数据分析、预处理

方法:

1.     背景

磁共振波谱（MRS）是一种非侵入性技术，用于量化人脑中的代谢物，以诊断各种疾病。然而，获取的 MRS 信号通常需要进行数据预处理和定量分析，以获得准确的代谢物浓度。目前，有多种开源工具可用于 MRS 信号的预处理、量化和分析。虽然这些工具提供了友好的用户界面，但仍要求用户编译源代码、下载依赖项或安装软件。此外，这些工具都不包括深度学习算法，这对当前人工智能时代的研究是一个重大限制。在过去的几十年中，已有多个 MRS 云平台用于模拟基集、从核磁共振的非采样数据中重建频谱。云平台还被应用于磁共振成像（MRI）。云计算提供了一个易于访问、灵活且可扩展的平台。用户无需担心硬件维护和管理，因此可以专注于各自专业领域的核心任务。

图1. 系统概念图

2.     工作流

  目前，该平台主要包括两个功能模块：智能量化和自动分析。用户可以注册账户或使用我们的测试账户（用户名：demo_csg，密码：csg12345678！）。主页上的使用手册也能帮助用户快速上手。CloudBrain-MRS 的工作流程如图 2 所示，详细描述如下：
(1) 加载数据和相应参数。该平台目前支持读取飞利浦、西门子和GE的 RAW 类型的数据，以及联影和西门子的 DICOM 类型的数据，还支持 LCModel 的数据格式。
(2)调用量化模型对数据进行批量或非批量量化，并保存量化结果。如果用户选择对数据进行再处理，则会在量化前进行去噪处理。
(3) 根据量化结果生成四种可视化谱图： 输入谱、整体和单个代谢物的拟合谱以及三维可视化谱。如果应用了去噪，则会显示去噪前和去噪后的谱图。
(4) 提取定量分析结果并生成相应的分析图表。统计分析将生成箱型图和三线表。一致性分析功能将生成Bland-Altman图和箱形图。

图2. 平台的整个工作流

3.     系统架构

  为了提高用户友好性，CloudBrain-MRS采用了浏览器/服务器（B/S）工作模式，即浏览器-请求/服务器-响应模式。系统架构可分为三个部分，即浏览器、服务器和数据库。图 3 显示了各部分之间的交互以及它们所依赖的库。

图3. 系统架构

4.     系统安全和隐私

  在云系统中，上传的文件会进行脱敏处理，姓名等敏感信息会被删除，但年龄和性别等数据分析所需的信息会被保留。患者隐私由浏览器端处理，不会向我们的服务器传输可识别患者身份的信息。用户有权删除数据。一旦删除，原始数据和处理过的数据都将从服务器上永久删除。
  在数据安全传输方面，该平台采用加密传输和身份验证等措施，防止敏感信息被非法获取。平台采用 2048 位 Rivest-Shamir-Adleman 算法（RSA）对敏感信息进行加密后再传输。JSON 网络令牌（JWT）用于验证用户的登录状态。利用高级加密标准（ASE）算法，JWT 通过加密传输进行安全验证。在数据存储安全方面，该平台设置了允许端口白名单，对数据库实施严格的访问限制，并增加了对分布式拒绝服务（DDOS）攻击的保护。数据库和缓存中的数据使用加密存储。

5.     云平台处理结果

  我们通过一些活体数据来展示该平台的实用性。

图4. 一名健康志愿者的 QNet 定量结果。该波谱在量化前已去噪。
图5. 12名健康志愿者和 14 名 MCI 患者的统计分析结果。为查看其他指标的方框图，设计了一个底部滑动标签栏。P 值小于 0.05 的组别会被平台标记。
图6. 12 名健康志愿者和 14 名MCI 患者的独立样本 t 检验结果。数据以均数 ± 标准差表示。
图7. 15 个健康志愿者的体内波谱关于 tNAA/tCr 的 Bland-Altman 分析。每个正方形代表每个光=波谱的量化结果。横轴和纵轴分别表示两种量化方法量化结果的平均值和差值。

致谢:

  本工作得到了国家重点研发计划（2023YFF0714200）、国家自然科学基金（62122064、61971361、62331021、62371410）、福建省自然科学基金（2023J02005、2021J011184）、厦门大学校长基金（20720220063）和厦门大学南强杰出人才计划的部分资助。感谢中国移动提供的云计算服务支持。感谢飞利浦公司的武志刚、林良杰和王家正，以及联影公司的朱家煜和张熙靖提供的技术支持。还要感谢 Stephen W. Provencher 公开了 LCModel的源码。

 

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