Foundational Model for Real-Time Neuroimaging Spatial Normalization

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.