波谱学杂志 ›› 2018, Vol. 35 ›› Issue (3): 303-317.doi: 10.11938/cjmr20182626

• 研究论文 • 上一篇    下一篇

基于Gadgetron平台的多GPU分布式磁共振图像重建

徐嘉文1,2,3, 徐健3, 周晓东3, 张聪3, 陈群3   

  1. 1. 高端医学影像技术研究中心(中国科学院 上海高等研究院), 上海 201210;
    2. 中国科学院大学, 北京 100049;
    3. 上海联影医疗科技有限公司, 上海 201807
  • 收稿日期:2018-03-28 出版日期:2018-09-05 发布日期:2018-08-28
  • 通讯作者: 陈群,Tel:021-67076980,E-mail:qun.chen@united-imaging.com E-mail:qun.chen@united-imaging.com
  • 基金资助:
    中国科学院重点部署项目(Y325511211).

Multi-GPU Distributed Magnetic Resonance Image Reconstruction Based on Gadgetron

XU Jia-wen1,2,3, XU Jian3, ZHOU Xiao-dong3, ZHANG Cong3, CHEN Qun3   

  1. 1. Center for Advanced Medical Imaging Technology(Shanghai Advanced Research Institute, Chinese Academy of Sciences), Shanghai 201210, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Shanghai United Imaging Healthcare Cooperation, Shanghai 201807, China
  • Received:2018-03-28 Online:2018-09-05 Published:2018-08-28

摘要: 为了满足磁共振成像(MRI)临床扫描的需求,磁共振图像重建算法的开发一直在不断进行.目前广泛使用的算法实现方式是利用中央处理器(CPU)对磁共振扫描数据进行数学变换得到图像,随着算法复杂度的提升,计算性能问题逐渐显露.利用CPU在大数据量下执行复杂算法时,计算并行性的缺失以及运算中产生的海量数据的存储负荷会导致计算变得极为缓慢,使得一些算法因为重建时间过长,在临床上面临难以推广的问题,也制约了基础研究中新算法的研发.本文设计并实现了一种新的重建算法执行方式,利用Gadgetron磁共振软件重建平台在多核CPU基础上搭载多块图形处理器(GPU),将磁共振图像重建以分布式并行计算方式实现,并以重建耗时较长的3D径向数据采集Stack of Star(SOS)的图像重建为实例,展示这种重建的实现方法能以相对低廉的硬件成本极大提升重建的速度.

关键词: 磁共振图像重建, Gadgetron, 多GPU, 分布式并行计算, 3D磁共振成像

Abstract: Clinical magnetic resonance imaging (MRI) scanning requires fast image reconstruction. At present, most image reconstruction algorithms are implemented with central processing unit (CPU)-based processing. However, with massive amount of raw data and increasing complexity of the algorithm, CPU-based processing can be inefficient due to lack of computation parallelism and slow computation speed. For this reason, some advanced algorithms are difficult to implement in clinical practice. In this paper, a new image reconstruction algorithm was designed and implemented, which combined multiple cores CPU with multi-graphic processing unit (GPU) to reconstruct magnetic resonance images by distributed parallel computing on the Gadgetron software platform. Taking the Stack of Star (SOS) reconstruction as an example, it was demonstrated that the proposed method improved the reconstruction speed significantly without the requirement of expensive hardware.

Key words: magnetic resonance image reconstruction, Gadgetron, multi-GPU, distributed parallel computing, 3D MRI

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