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Abstract #0013

Foundational Model for Real-Time Neuroimaging Spatial Normalization

Yongkai Liu1, Theo Chiang1, Helena Feng1, Sally Luo1, Jade Zhang1, Shuo Li2, Mike Moseley1, and Greg Zaharchuk1
1Radiology, Stanford University, Palo Alto, CA, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States

Synopsis

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.

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