Multi-Modality Generative Intelligence Framework for Alzheimer's Disease Quantification

Abstract

Dementia, particularly Alzheimer's Disease (AD), is a major global health challenge and a leading cause of death and disability. As projections suggest that the prevalence of dementia could triple by 2050, the need for precise quantification and effective management of AD becomes increasingly critical. Accurately quantifying and measuring AD can not only significantly extend the life expectancy of patients, but also plays a pivotal role in unraveling the complex mechanisms of AD and advancing our understanding of the disease, paving the way for the development of novel therapeutic approaches.

Quantifying AD involves the analysis of biomarkers, imaging findings, and clinical parameters, with neuroimaging techniques MRI and PET being the most important. Each technique provides unique and complementary information for understanding and managing AD. MRI offers detailed structural brain images, while PET reveals information on metabolic activity and protein deposits. Although integrating these technologies has advantages, challenges and inconsistencies exist in the AD quantification and diagnosis process. These challenges include ensuring PET quantification accuracy, maintaining the integrity of spatial distribution patterns in the imaging and exploring the heterogeneity among patients, and enhancing the accessibility and quality of multimodal images.

To address the identified challenges in enhancing AD quantification, we propose a framework that includes three key components: 1) a refined tracer-specific PET quantification approach, designed to improve the accuracy and reliability of biomarker measurements in neuroimaging studies; 2) an intelligent diagnostic model that maintains the integrity of 3D volumetric data and a comprehensive analysis of heterogeneous patterns using model-derived saliency maps; 3) a generative intelligent model that supplements clinical-usable multimodal data, thereby compensating for the limited availability of comprehensive imaging datasets.