Introduction

DICOM-RST is a robust DICOMweb-compatible gateway server that supports QIDO-RS, WADO-RS and STOW-RS independently of the PACS vendor, ensuring robust and performant transfers of large amounts of imaging data with high parallelism from multiple PACS to multiple clients.

This project is part of the Open Medical Inference methodology platform.

The OMI methodology platform aims to improve the quality of medical diagnoses and treatment decisions by using artificial intelligence (AI) to simplify time-consuming and repetitive tasks in medicine. To improve medical care, OMI is developing an open protocol for data exchange on the common framework of the Medical Informatics Initiative (MII). The project team is also actively involved in the MII interoperability working group.

OMI uses innovative methods to make AI models remotely usable for different hospitals. For example, the project is creating the technical requirements for a hospital to be able to use the AI of other hospitals to analyze image data - without having to keep it in its own data center. The semantically interoperable exchange of multimodal healthcare data is also to be facilitated. OMI is particularly focused on image-based multimodal AI models, which have the potential to achieve significant progress in the field of medical research and care. Funding is provided by the Federal Ministry of Education and Research (BMBF).

This documentation provides a reference for the configuration file, the provided endpoints and a user guide for installation and troubleshooting.