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

August 21, 2024 updated by: Professor Winnie W.C. Chu, Chinese University of Hong Kong

Modelling Tau Deposition and Distribution From Diffusion Tensor Imaging With Generative Adversarial Network for Alzheimer's Disease Diagnosis

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.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

250

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Hong Kong
    • Shatin
      • Hong Kong, Shatin, Hong Kong
        • Recruiting
        • The Chinese University of Hong Kong, Prince of Wale Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

55 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

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.

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Controls
Mild cognitive impairment
AD dementia

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Structural similarity index to measure the similarity between synthetic image and ground truth for 20% of data in testing set
Time Frame: Through study completion, an average of 1 year
Through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese University of Hong Kong

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 30, 2021

Primary Completion (Estimated)

June 29, 2025

Study Completion (Estimated)

December 31, 2025

Study Registration Dates

First Submitted

August 4, 2021

First Submitted That Met QC Criteria

August 23, 2021

First Posted (Actual)

August 25, 2021

Study Record Updates

Last Update Posted (Actual)

August 22, 2024

Last Update Submitted That Met QC Criteria

August 21, 2024

Last Verified

August 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2021.243

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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