Keywords: Data Processing, Alzheimer's Disease, Amyloid Related Imaging Abnormalities
Motivation: Using anti-amyloid monoclonal antibodies to treat Alzheimer’s disease (AD) can lead to ‘Amyloid-Related Imaging Abnormalities’ (ARIA), indicative of oedema (ARIA-E). ARIA-E's transient nature poses diagnostic challenges, necessitating efficient detection and monitoring, which is critical for drug development for AD.
Goal(s): This study aims to develop methods for quantifying and tracking ARIA-E across multiple longitudinal scans, including subtle manifestations.
Approach: Temporal variance and gradient maps were computed using serial ultra-rapid 3D FLAIR scans. Data-driven methods were employed to assess statistical significance of changes.
Results: The proposed maps highlight significant regions of change in the presence of varying levels of ARIA-E.
Impact: This work has facilitated the tracking of statistically significant ARIA-E over multiple serial scans, which will enable further automatic detection methods. These methods may inform dose adjustment and patient safety measures, which is important as anti-amyloid monoclonal antibodies become commonplace.
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