Deep Learning-Based Prediction of PET Amyloid Status Using Multi-Contrast MRI

Motivation: Accurate amyloid-beta positivity prediction is essential for identifying patients for Alzheimer's disease trials and treatments, and T1w only MRI-based predictions have showed moderate performance. Goal(s): To evaluate whether adding T2-FLAIR to T1w imaging enhances deep learning model performance for predicting amyloid PET positivity. Approach: Two EfficientNet models were trained on 4,058 multi-contrast MRI exams and validated using internal and external test sets, with statistical comparison of T1w-only and T1w+T2-FLAIR inputs. Results: The T1w+T2-FLAIR model significantly improved PET-based amyloid status prediction, showing robustness across internal and external test sets. Activation maps highlighted brain regions, particularly around ventricles, linked to white matter abnormalities. Impact: Adding T2-FLAIR to T1w MRI in deep learning models significantly improves amyloid PET positivity prediction, aiding early Alzheimer's disease detection. This approach enhances non-invasive opportunistic screening, potentially streamlining patient selection for clinical trials and targeted treatments.