MRI-free amyloid PET quantification using a deep learning model and white matter reference

Background Accurate quantification of standardized uptake value ratio (SUVR) in amyloid PET is essential for Alzheimer's disease (AD) diagnosis but typically requires MRI-based segmentation due to subtle uptake differences between gray and white matter. Objective This study aimed to develop and validate a 3-dimensional deep learning model capable of segmenting these tissues directly from PET images to enable MRI-free SUVR quantification for AD diagnosis. Methods This retrospective study included 385 participants who underwent brain amyloid PET and MRI. After excluding 12 data-corrupted cases, 373 subjects were divided into training (n = 318) and test (n = 55) sets. External validation used 625 PET/CT scans from the Alzheimer's Disease Neuroimaging Initiative. Model performance was assessed using Dice coefficients and intersection over union. PET-based SUVRs derived from model-generated masks were compared with MRI-based SUVRs using Spearman correlation, and their diagnostic utility was evaluated by group differences and receiver operating characteristic analysis. Results The model achieved high Dice coefficients for grey matter (GM; 0.785 internal, 0.743 external) and white matter (WM; 0.838 internal, 0.803 external). PET/CT-based SUVR values strongly correlated with MRI references (Spearman's ρ ≥ 0.98, p < 0.001). PET/CT-derived SUVR GM and SUVR GM/WM predicted amyloid status (AUC 0.86 and 0.85, respectively) and cognitive impairment (AUC 0.78). Conclusions Deep learning–based amyloid PET segmentation enables accurate MRI-free quantification of gray and white matter SUVRs. This approach simplifies clinical workflow while maintaining diagnostic performance comparable to MRI-based methods for AD diagnosis.