Summary of the project
The project integrates an MRI simulator named ‘eduMRIsim’ to address the lack of access to MRI scanners at the TU/e university campus. This simulator provides a realistic MRI user interface (UI), allowing students to interact with different scan modalities, tune scan parameter settings and view the resulting image appearance and quality. Furthermore, with the simulation software students are able to introduce and reduce artifacts in the images by selecting different image acquisition or processing techniques. The MRI simulator even allows students to perform a realistic MRI examination of a patient, consisting of steps such as selecting an anatomical model, entering an examination date and name, planning of the scans and viewing and exporting the acquired images and metadata in multiple file formats. Instead of passive learning, students have been able to generate their own MRI data and handle challenges that arise during the process.
Aim of the project
The project's aim has been to develop challenge-based learning exercises and labs centered around the MRI simulator. It has been focused on significantly enhancing students' understanding of MR imaging, from physical and physiological principles to virtually executing the entire MRI workflow. The MRI simulator has been utilized to provide an enhanced experience of this complex imaging technique, especially beneficial as most students do not usually have access to an MRI scanner for learning purposes.
Results and learnings
- Understand and explore the relationship between MRI scan parameter settings and the resulting image contrast and quality.
- Understand and explore the techniques and clinical or research-based applications of different MRI modalities.
- Get acquainted with the whole scanning procedure workflow: patient preparation, scan planning, actual scanning, image viewing and manipulation.
- Be able to conduct a clinical MRI study by acquiring images from two populations, processing them and calculate group statistics.
- Learn how to deal with physiological or hardware-related artifacts by changing MRI protocols or applying image processing.
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