上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 10: 1629 - 1636

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2024.099

电子信息与电气工程

医疗PACS影像系统的数据存储性能优化

  • 尤丽珏 ,
  • 焦圣品 ,
  • 李小勇
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  • 1.复旦大学附属华东医院 计算机中心,上海 200040
    2.上海霄云信息科技有限公司,上海 200240
    3.上海交通大学 电子信息与电气工程学院,上海 200240
尤丽珏(1975—),高级工程师,从事医院数字化研究.
李小勇,副教授;E-mail:xiaoyongli@sjtu.edu.cn.

收稿日期: 2024-03-21

  修回日期: 2024-06-07

  录用日期: 2024-06-28

  网络出版日期: 2024-11-01

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Optimization of Data Storage Performance in Medical PACS Imaging System

  • YOU Lijue ,
  • JIAO Shengpin ,
  • LI Xiaoyong
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  • 1. Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
    2. Shanghai Xiaoyun Info Tech Co., Ltd., Shanghai 200240, China
    3. School of Electronic andElectrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2024-03-21

  Revised date: 2024-06-07

  Accepted date: 2024-06-28

  Online published: 2024-11-01

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摘要

医疗影像归档与通信系统(PACS)影像数据是典型的海量小文件场景,其面临的挑战主要为高效的海量元数据管理和有效解决碎片化导致的性能下降.通过分析医疗PACS影像存储系统路径的全IO(input/output)各个关键环节,从PACS软件调阅算法、存储协议网关高并发设计、小文件聚合以及数据存储服务并发模型4个维度进行优化设计,实现对PACS调阅性能的大幅提升.实际测试表明,优化设计后PACS影像系统的调阅性能可达到每秒300幅图像,为传统存储调阅速度的3倍以上,有效解决了PACS影像数据调阅卡顿的问题.

关键词： 海量小文件; PACS影像; 分布式存储; 性能优化

本文引用格式

尤丽珏 , 焦圣品 , 李小勇 . 医疗PACS影像系统的数据存储性能优化[J]. 上海交通大学学报, 2024 , 58(10) : 1629 -1636 . DOI: 10.16183/j.cnki.jsjtu.2024.099

Abstract

Medical picture archiving and communication system (PACS) is a typical application scenario with massive small files, which faces two challenges in data storage, i.e., efficient metadata management and effective performance reduction caused by fragmentization. By analyzing various key components of the full IO (input/output) path in the medical PACS imaging system, this paper optimizes the design to achieve a significant improvement in the retrieval performance of the PACS from four dimensions, PACS software retrieval algorithm, storage protocol gateway high-concurrency design, small file aggregation, and data storage service concurrency model. The results of test show that the retrieval performance after optimizion can reach 300 images per second, which is more than three times that of traditional storage, and resolves effectively the performance problem of PACS image data retrieval.

Key words： massive small files; picture archiving and communication system (PACS) image; distributed storage; performance optimization

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