Radpoint Sp. z o.o., Ceglana 35, 40-514 Katowice, Poland.
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Sensors (Basel). 2022 Nov 7;22(21):8569. doi: 10.3390/s22218569.
The limitations of the classic PACS (picture archiving and communication system), such as the backward-compatible DICOM network architecture and poor security and maintenance, are well-known. They are challenged by various existing solutions employing cloud-related patterns and services. However, a full-scale cloud-native PACS has not yet been demonstrated. The paper introduces a vendor-neutral cloud PACS architecture. It is divided into two main components: a cloud platform and an access device. The cloud platform is responsible for nearline (long-term) image archive, data flow, and backend management. It operates in multi-tenant mode. The access device is responsible for the local DICOM (Digital Imaging and Communications in Medicine) interface and serves as a gateway to cloud services. The cloud PACS was first implemented in an Amazon Web Services environment. It employs a number of general-purpose services designed or adapted for a cloud environment, including Kafka, OpenSearch, and Memcached. Custom services, such as a central PACS node, queue manager, or flow worker, also developed as cloud microservices, bring DICOM support, external integration, and a management layer. The PACS was verified using image traffic from, among others, computed tomography (CT), magnetic resonance (MR), and computed radiography (CR) modalities. During the test, the system was reliably storing and accessing image data. In following tests, scaling behavior differences between the monolithic Dcm4chee server and the proposed solution are shown. The growing number of parallel connections did not influence the monolithic server's overall throughput, whereas the performance of cloud PACS noticeably increased. In the final test, different retrieval patterns were evaluated to assess performance under different scenarios. The current production environment stores over 450 TB of image data and handles over 4000 DICOM nodes.
经典 PACS(影像归档和通信系统)存在诸多局限性,例如向后兼容的 DICOM 网络架构、安全性和维护性差等,这些局限性是众所周知的。各种采用云相关模式和服务的现有解决方案对其提出了挑战。然而,尚未全面展示全规模的云原生 PACS。本文介绍了一种与供应商无关的云 PACS 架构。它分为两个主要组件:云平台和访问设备。云平台负责近线(长期)图像存档、数据流和后端管理。它以多租户模式运行。访问设备负责本地 DICOM(医学数字成像和通信)接口,并充当云服务的网关。云 PACS 最初在 Amazon Web Services 环境中实现。它采用了许多专为云环境设计或改编的通用服务,包括 Kafka、OpenSearch 和 Memcached。还开发了一些定制服务,例如中央 PACS 节点、队列管理器或流程工作器,作为云微服务,提供 DICOM 支持、外部集成和管理层。该 PACS 使用来自 CT(计算机断层扫描)、MR(磁共振)和 CR(计算机射线照相)等模态的图像流量进行了验证。在测试过程中,系统可靠地存储和访问图像数据。在后续测试中,展示了单片 Dcm4chee 服务器和所提出的解决方案之间的扩展行为差异。连接数量的增加并没有影响单片服务器的整体吞吐量,而云 PACS 的性能明显提高。在最后的测试中,评估了不同的检索模式,以评估不同场景下的性能。当前的生产环境存储了超过 450 TB 的图像数据,并处理了超过 4000 个 DICOM 节点。