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

NCT05020626 | Recruiting

• 1 other identifier: 2021.243
• Study Type: observational
• Target: 250
• Locations: 1 country
• Sites: 1

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.

Conditions

Alzheimer's Disease Diagnosis

Keywords

Generative Adversarial Network; Tau; Diffusion Tensor Imaging

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (3)

Controls

Mild cognitive impairment

AD dementia

Eligibility Criteria

• Age: 55 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The data will be collected from a public database, i.e, Alzheimer's Disease Nueroimaging Initiative (ADNI). According to their recruitment protocol at http://adni.loni.usc.edu/methods/documents/, the study enrolls men and women aged 55-90 years across MCI and mild AD dementia participant groups, and control normal aged 65 years with exceptions granted for minority participants.

You may qualify if:

• 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.

You may not qualify if:

• AD with mixed dementia
• Non-AD dementia
• History of severe traumatic brain injury, severe depression, stroke, brain tumors, and incident major systemic illness

Sponsors & Collaborators

• Chinese University of Hong Kong: lead

Study Sites (1)

The Chinese University of Hong Kong, Prince of Wale Hospital

Hong Kong, Shatin, Hong Kong

RECRUITING

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MeSH Terms

Conditions

Pick Disease of the Brain

Condition Hierarchy (Ancestors)

Frontotemporal Dementia; Frontotemporal Lobar Degeneration; Dementia; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurocognitive Disorders; Mental Disorders

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: August 4, 2021
• First Posted: August 25, 2021
• Study Start: June 30, 2021
• Primary Completion: June 29, 2025
• Study Completion: December 31, 2025
• Last Updated: August 22, 2024

Record last verified: 2024-08

Locations

HK (1)