Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Alzheimer's Disease Diagnosis. Basic parameters: from 55 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Hong Kong
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Modelling Tau Deposition and Distribution From Diffusion Tensor Imaging With Generative Adversarial Network for Alzheimer's Disease Diagnosis
Overview
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.
Primary outcome measures
- 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]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
Hong Kong · 1 center
- The Chinese University of Hong Kong, Prince of Wale Hospital — Hong Kong
Publications
- Lane CA, Hardy J, Schott JM. Alzheimer's disease. Eur J Neurol. 2018 Jan;25(1):59-70. doi: 10.1111/ene.13439. Epub 2017 Oct 19. PMID 28872215
- Alzheimer's Association. 2016 Alzheimer's disease facts and figures. Alzheimers Dement. 2016 Apr;12(4):459-509. doi: 10.1016/j.jalz.2016.03.001. PMID 27570871
- Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 2011 Sep;10(9):819-28. doi: 10.1016/S1474-4422(11)70072-2. Epub 2011 Jul 19. PMID 21775213
- Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr, Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic gu PMID 21514248
- Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM. Alzheimer's disease. Lancet. 2016 Jul 30;388(10043):505-17. doi: 10.1016/S0140-6736(15)01124-1. Epub 2016 Feb 24. PMID 26921134
- Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, Dickson DW, Duyckaerts C, Frosch MP, Masliah E, Mirra SS, Nelson PT, Schneider JA, Thal DR, Thies B, Trojanowski JQ, Vinters HV, Montine TJ. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement. 2012 Jan;8(1):1-13. doi: 10.1016/j.jalz.2011.10.007. PMID 22265587
- Khan TK. An Algorithm for Preclinical Diagnosis of Alzheimer's Disease. Front Neurosci. 2018 Apr 30;12:275. doi: 10.3389/fnins.2018.00275. eCollection 2018. PMID 29760644
- Mito R, Raffelt D, Dhollander T, Vaughan DN, Tournier JD, Salvado O, Brodtmann A, Rowe CC, Villemagne VL, Connelly A. Fibre-specific white matter reductions in Alzheimer's disease and mild cognitive impairment. Brain. 2018 Mar 1;141(3):888-902. doi: 10.1093/brain/awx355. PMID 29309541
Identifiers
NCT: NCT05020626 · 2021.243