Performance of Medical Image Transfer in High Bandwidth Networks

Pyciński, Bartłomiej; Kawa, Jacek; Bozek, P; Smoliński, Michał; Bieńkowska, Maria

doi:10.1007/978-3-030-15472-1_4

Cited by 1 publication

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“…On the PACS level, different throughputs might be observed for different instance sizes and compression methods (see, for example, Section 4.3 ). For a reliable evaluation, one can compare the performance of the same software on different devices or run various programs in an identical environment [ 57 ]. On the other hand, despite using the monolithic reference of Dcm4chee in the test depicted in Section 4.2 to show the behavior difference under growing load in a similar cloud environment, we do not claim it under- or outperforms this solution on general terms, as neither the reference nor the presented environment was explicitly optimized for performance.…”

Section: Discussionmentioning

confidence: 99%

Design and Implementation of a Cloud PACS Architecture

Kawa

Pyciński

Smoliński³

et al. 2022

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%