Langkammer, Christian, Vaclavu, Lena, Kuestner, Thomas, Bauer, Mel, Salameh, Najat, Palombo, Marco ![]() Item availability restricted. |
![]() |
PDF
- Accepted Post-Print Version
Restricted to Repository staff only until 28 June 2025 due to copyright restrictions. Download (69kB) |
Abstract
Magnetic resonance imaging (MRI) plays a key role in modern radiology with at least 100 million diagnostic scans performed annually across the globe. However, besides its undeniable value, the (multivariate) biophysical mechanisms underlying MRI contrast generation are rather unclear and subject of current research. Furthermore, with the advent of AI into all areas of medical imaging, MRI users now tend to employ black-box models in image reconstruction, segmentation, and disease classification, which in turn introduces an additional challenge in trust and interpretation of the results. Therefore, as part of this focus topic, we believe it is paramount to also shed light on the application of AI for MRI data interpretation, emphasizing the importance of explainable AI (xAI) in validating and understanding the obtained results. In addition, the educational sessions will cover the use of postmortem MRI studies to validate tissue models and the innovative approaches in biomarker discovery that rely on accurate and validated quantitative MRI techniques. Moreover, dedicated sessions will discuss the emerging applications of low-field MRI, and the vital role of validation.
Item Type: | Article |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Psychology Cardiff University Brain Research Imaging Centre (CUBRIC) |
Publisher: | Springer |
ISSN: | 0968-5243 |
Date of First Compliant Deposit: | 26 July 2024 |
Date of Acceptance: | 8 May 2024 |
Last Modified: | 08 Nov 2024 10:45 |
URI: | https://orca.cardiff.ac.uk/id/eprint/170935 |
Actions (repository staff only)
![]() |
Edit Item |