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Design and Implementation of a Cloud PACS Architecture

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...

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Autores principales: Kawa, Jacek, Pyciński, Bartłomiej, Smoliński, Michał, Bożek, Paweł, Kwasecki, Marek, Pietrzyk, Bartosz, Szymański, Dariusz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654824/
https://www.ncbi.nlm.nih.gov/pubmed/36366266
http://dx.doi.org/10.3390/s22218569
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author Kawa, Jacek
Pyciński, Bartłomiej
Smoliński, Michał
Bożek, Paweł
Kwasecki, Marek
Pietrzyk, Bartosz
Szymański, Dariusz
author_facet Kawa, Jacek
Pyciński, Bartłomiej
Smoliński, Michał
Bożek, Paweł
Kwasecki, Marek
Pietrzyk, Bartosz
Szymański, Dariusz
author_sort Kawa, Jacek
collection PubMed
description 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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spelling pubmed-96548242022-11-15 Design and Implementation of a Cloud PACS Architecture Kawa, Jacek Pyciński, Bartłomiej Smoliński, Michał Bożek, Paweł Kwasecki, Marek Pietrzyk, Bartosz Szymański, Dariusz Sensors (Basel) Article 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. MDPI 2022-11-07 /pmc/articles/PMC9654824/ /pubmed/36366266 http://dx.doi.org/10.3390/s22218569 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kawa, Jacek
Pyciński, Bartłomiej
Smoliński, Michał
Bożek, Paweł
Kwasecki, Marek
Pietrzyk, Bartosz
Szymański, Dariusz
Design and Implementation of a Cloud PACS Architecture
title Design and Implementation of a Cloud PACS Architecture
title_full Design and Implementation of a Cloud PACS Architecture
title_fullStr Design and Implementation of a Cloud PACS Architecture
title_full_unstemmed Design and Implementation of a Cloud PACS Architecture
title_short Design and Implementation of a Cloud PACS Architecture
title_sort design and implementation of a cloud pacs architecture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654824/
https://www.ncbi.nlm.nih.gov/pubmed/36366266
http://dx.doi.org/10.3390/s22218569
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