Technology | May 05, 2009

DeJarnette's Intelligent Router Gives Images Direction

DeJarnette's Intelligent Router solution, which provides DICOM study distribution capability, provides manual routing and three automated routing technologies:
- Source Metadata Routing (SMR)
- Modality Worklist Routing (MWR)
- Query/Move Routing (QMR)

SMR ("store and forward" routing) determines the destination of a study by applying routing rules based on metadata in the source data stream. Metadata mapping, addition and substitution rules are supported.

MWR ("prefetch" routing) makes use of a source modality worklist provider to determine the studies to route. MWR provides the ability to apply "prefetch rules" based on queriable DICOM metadata fields (modality, study date, time, etc.), most recent 'N', and date range. QMR ("Nighthawk" routing) makes use of a source Q/R provider to determine the studies to route. QMR makes use of a schedule and queriable metadata fields to determine what to route. In both cases, routed studies are transfered to the destination either via a third party move or a retrieve and store operation (allowing for cascading of the SMR router).

All routers make use of schedule rules and support unlimited multiple input sources and unlimited multiple destinations. Image compression/decompression is provide as an option. The Intelligent Router is available as a turnkey solution or as a PACSware (software only) solution.

For more information:

Related Content

DR 800 multi-purpose digital imaging system with Dynamic Musica
News | Digital Radiography (DR) | July 20, 2018
Agfa displayed the new DR 800 multi-purpose digital imaging system with Dynamic ...
Fujifilm to Host Pediatric Imaging Best Practices Symposium at AHRA 2018
News | Pediatric Imaging | July 18, 2018
Fujifilm Medical Systems U.S.A. Inc. announced that it will offer educational opportunities and exhibit its latest...
Study Points to Need for Performance Standards for EHR Usability and Safety
News | Electronic Medical Records (EMR) | July 18, 2018
A novel new study provides compelling evidence that the design, development and implementation of electronic health...
Guerbet, IBM Watson Health Partner on Artificial Intelligence for Liver Imaging
News | Clinical Decision Support | July 10, 2018
Guerbet announced it has signed an exclusive joint development agreement with IBM Watson Health to develop an...
Sponsored Content | Case Study | PACS | July 09, 2018
One of the Northeast’s major teaching hospitals is an international leader in virtually every area of medicine. It has...
Sponsored Content | Whitepapers | PACS | July 09, 2018
The move toward value-based reimbursement (VBR) models is putting pressure on healthcare organizations to modernize...
FDA Clears Bay Labs' EchoMD AutoEF Software for AI Echo Analysis
Technology | Cardiovascular Ultrasound | June 19, 2018
Cardiovascular imaging artificial intelligence (AI) company Bay Labs announced its EchoMD AutoEF software received 510(...
News | Remote Viewing Systems | June 14, 2018
International Medical Solutions (IMS) recently announced that the American College of Radiology (ACR) added IMS'...
Wake Radiology Launches First Installation of EnvoyAI Platform
News | Artificial Intelligence | June 13, 2018
Artificial intelligence (AI) platform provider EnvoyAI recently completed their first successful customer installation...
How AI and Deep Learning Will Enable Cancer Diagnosis Via Ultrasound

The red outline shows the manually segmented boundary of a carcinoma, while the deep learning-predicted boundaries are shown in blue, green and cyan. Copyright 2018 Kumar et al. under Creative Commons Attribution License.

News | Ultrasound Imaging | June 12, 2018 | Tony Kontzer
June 12, 2018 — Viksit Kumar didn’t know his mother had...
Overlay Init