ArticleSensors (Basel, Switzerland)2022
Design and Implementation of a Cloud PACS Architecture.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Iris RESTful Server and IrisTileSource: An Iris implementation for existing OpenSeaDragon viewers.Journal of pathology informatics · 2026Article
- Technical requirements and optimization strategies for home-based teleradiology workstations: a review article.Insights into imaging · 2025Review
- Wearing a fur coat in the summertime: Should digital pathology redefine medical imaging?Journal of pathology informatics · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.