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Abstract #4222

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

Donghoon Kim1, Jon André Ottessen2, Ashwin Kumar1, Brandon C. Ho1, Christina B. Young3, Elizabeth Mormino3, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2University of Oslo, Oslo, Norway, 3Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease

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.


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Keywords