This software can be set up as a module on Mercure DICOM router, which must also be cofigured on an AWS EC2 instance. It will take the fMRI images as input and apply an ANTs motion correction algorithm in 4D.
plasomicsi/mercure-motioncorr_nii:latest
Mercure is a robust web-based orchestration system designed to manage and monitor various services within a DICOM network. The application provides real-time insights into system status, server health, and service management, making it an essential tool for medical imaging and healthcare IT infrastructures.
- Service Monitoring: Continuously track the status of key components such as the Receiver, Router, Processor, Dispatcher, Cleaner, and Bookkeeper.
- Server Health: Overview of server resources including disk space usage to ensure smooth operation.
- Configuration Management: Manage settings and configurations directly from the web interface.
- Security: Integrated TLS support ensures that all data transmitted over the network is secure.
To get started with Mercure, clone this repository (or download from Docker) and follow the setup instructions provided in the official Mercure documentation.
The main page displays the system status overview of the Mercure web application. It shows the operational status of various services and server health in terms of disk space availability.
The server must be connected to the Orthanc server, where the processed images can be finally downloaded. It provides detailed configuration options for DICOM nodes, including TLS security settings.
This section highlights the performance improvement brought about by the motion correction module implemented in the Mercure system. The graph below illustrates the average residuals between successive volumes for the original DWI image and after applying two different parameters of the motion correction.
The blue line represents the residuals for the original DWI images, showing higher values initially which suggest less accuracy in the volume alignment. The orange and yellow lines represent the residuals after applying two different configurations of the motion correction algorithm. Both show significantly reduced residuals, indicating a marked improvement in alignment accuracy across the series of volumes.
These results underscore the effectiveness of our motion correction techniques in enhancing the precision of medical imaging data analysis.
Contributions are welcome! Please fork this repository and submit a pull request with your enhancements. For major changes, please open an issue first to discuss what you would like to change.