Keywords: Analysis/Processing, Analysis/Processing
Motivation: Traditional spatial normalization methods like SPM12 are slow and struggle with large datasets, limiting their use in clinical settings that require rapid processing, especially for acute stroke.
Goal(s): To develop a fast, scalable foundational deep learning model for spatial normalization across all MRI sequences
Approach: Using a clinical dataset of 11,939 MR volumes across six common sequences, we trained a modified 3D U-Net with SPM12 results as the reference standard. Local normalized cross-correlation loss optimized training, and Dice Similarity Coefficient evaluated performance.
Results: The model achieved an overall DSC of 0.98 across sequences, processing each volume in 0.7 seconds—120 times faster than SPM12.
Impact: This foundation model represents the first AI method to standardize spatial normalization for a wide range of neuroimaging sequences, enabling real-time and consistent neuroimaging analyses for both clinical and research applications.
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