Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis
- Conditions
- Alzheimer's Disease Diagnosis
- Registration Number
- NCT05020626
- Lead Sponsor
- Chinese University of Hong Kong
- Brief Summary
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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 250
- With the age of 55 years and above
- With brain MRI taken within ±6 months from the date of clinically confirmed diagnosis of AD, MCI or normal cognition.
- AD with mixed dementia
- Non-AD dementia
- History of severe traumatic brain injury, severe depression, stroke, brain tumors, and incident major systemic illness
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Structural similarity index to measure the similarity between synthetic image and ground truth for 20% of data in testing set Through study completion, an average of 1 year
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
The Chinese University of Hong Kong, Prince of Wale Hospital
🇭🇰Hong Kong, Shatin, Hong Kong