Keywords: Analysis/Processing, Relaxometry
Motivation: Quantitative MRI has the potential for improved disease characterization, but the limited accessibility impedes its application.
Goal(s): To develop a deep learning method for retrospective T1 and T2 quantification from real-world brain MRI data, with the ability to handle diverse imaging protocols.
Approach: A protocol-aware self-supervised learning framework was developed, with the imaging parameters incorporated as additional inputs to the model.
Results: Validation on volunteers showed errors within 10% for nine brain regions when compared to prospective T1/T2 mapping. Application to 376 glioblastoma patients with diverse imaging protocols revealed statistical differences in T1 and T2 among tumor sub-regions and normal-appearing tissues.
Impact: The proposed method may allow retrospective T1 and T2 mapping in large real-world MRI datasets, enabling analysis of them regardless of the difference in protocols and scanners. This will facilitate the large-scale investigation of quantitative MRI as biomarkers for diseases.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords