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

NCT05020626 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 250

Last updated 2024-08-22

No results posted yet for this study

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

Sponsors & Collaborators

  • Chinese University of Hong Kong

    lead OTHER

Eligibility

Min Age
55 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-06-30
Primary Completion
2025-06-29
Completion
2025-12-31

Countries

  • Hong Kong

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05020626 on ClinicalTrials.gov