Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis

Recruiting now

Conditions studied: Alzheimer's Disease Diagnosis

In brief

The most significant impact of this project is to propose for the first time a novel generative adversarial network (GAN), as one kind of deep learning architecture, to automatically generate synthetic PET images reflecting tau deposition, from brain DTI images. If successful, this framework will become the most state-of-the-art approach to simulate the stereotypical pattern of intracerebral tau accumulation and distribution in vivo. Synthetic tau-PET images via DTI, possessing overwhelming superiority in radiation-free, non-invasiveness and cost-effectiveness, will potentially serve as one of alternative modalities of PET in detecting tau-load and probably outperform PET on accessibility, generalizability, and availability in future, making it much more attractive in clinical application. A big conceptual shift may occur preferring a fire-new tau-PET simulated via DTI. The DTI data-driven deep learning framework to be created in this project will constitute an accurate, robust, clinically applicable and explainable tool to efficiently categorize the subjects into tau-burden positive and tau-burden negative cases, which will undoubtedly contribute to both clinical and research activities.

Key facts

Study ID
NCT05020626
Run by
Chinese University of Hong Kong
People needed
250
Starts
2021-06-30
Expected to finish
2025-12-31
Last updated by the study team
2024-08-22

Who can join

Age: 55 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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