One of the challenges of creating a DICOM message is how to implement putting data in the correct place. Part of it is by inserting the data in the specific DICOM tags, while the other is to insert binary data such as a picture - In this article I will explain both.

To create a DICOM message, you can either use the EnsLib.DICOM.File class (to create a DICOM file) or the EnsLib.DICOM.Document class (to create a message that can be sent to PACS directly). In either case, the SetValueAt method will allow you to add your data to the DICOM tags.

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Is anyone using DICOM Interoperability in IRIS for Health configured in Mirror?

I'm asking because I'm not sure how to handle where the DICOM messages are stored.

For some reason DICOM use the filesystem to store raw messages, the directory used can be configured in the StorageLocation production settings, obviously this is a big issue if/when a mirror failover occur.

Unfortunately in IRIS it's not possible to change the DICOM storage from file stream to global stream.

Has anyone came across this issue?

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Hi,

I've been working on a very basic Interoperability production in my computer. I followed one of the examples in the courses and created a TEST.FileRouterRoutingRule, but when trying to edit this rule, it takes me to the Rule Editor Login Page, where I can't get in (Not even with the _SYSTEM credentials) - Is this functionality unavailable in the community edition?

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RabbitMQ is a message broker that allows producers (those who send a data message) and consumers (those who receive a data message) to establish asynchronous, real-time, and high-performance massive data flows. RabbitMQ supports AMQP (Advanced Message Queuing Protocol), an open standard application layer protocol.
The main reasons to employ RabbitMQ include the following:

  • You can improve the performance of the applications using an asynchronous approach.
  • It lets you decouple and reduce dependencies between services, microservices, and applications with the help of a data message mediator, meaning that there is no need for producers and consumers of exchanged data to know each other.
  • It allows the long-running processing of sent data (with the results) to be delivered after utilizing a response queue.
  • It helps you migrate from monolithic to microservices, where microservices exchange data via Rabbit in a decoupled and asynchronous way.
  • It offers reliability and resilience by making it possible for messages to be stored and forwarded. A message can be delivered multiple times until it is processed.
  • Message queueing is the key to scaling your application. As the workload increases, you will only have to add more workers to handle the queues faster.
  • It works well with data streaming applications.
  • It is beneficial for IoT applications.
  • It is a must for Bots’ communication.

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