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Abstract #3328

Assessing the clinical relevance of amyloid PET prediction from T1w MRI for Alzheimer’s disease

Louise Baron1,2, Ross Callaghan3, David M Cash4, Philip SJ Weston4, Hojjat Azadbakht3, and Hui Zhang2,5
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Hawkes Institute, University College London, London, United Kingdom, 3AINOSTICS ltd, Manchester, United Kingdom, 4Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 5Department of Computer Science, University College London, London, United Kingdom

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease, Amyloid PET, MRI to PET translation, PET synthesis

Motivation: Amyloid PET imaging is important for Alzheimer’s disease (AD) diagnosis but currently largely inaccessible. As an alternative, generating synthetic PET data from T1w MRI has been explored. However, current PET synthesis models lack rigorous evaluation in brain regions that are clinically relevant to AD pathology.

Goal(s): To provide a clinically focused evaluation of T1w-to-amyloid PET synthesis models for AD.

Approach: We will train a generative model to predict PET from T1w MRI and assess performance using standard metrics, alongside a clinical evaluation of reconstruction accuracy in brain regions where amyloid load has diagnostic importance for AD.

Impact: Our findings could clarify the clinically relevant performance of existing amyloid PET synthesis techniques for Alzheimer’s disease.

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Keywords