@article{27cd17555f6f447885d9de465291a4fe,
title = "Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification",
abstract = "Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.",
keywords = "Alzheimer's disease, PET, amyloid, brain, longitudinal, quantification, tau",
author = "Ariane Bollack and Pemberton, {Hugh G.} and Collij, {Lyduine E.} and Pawel Markiewicz and {on behalf on the AMYPAD consortium} and Cash, {David M.} and Gill Farrar and Frederik Barkhof",
note = "Funding Information: This work is supported by the EPSRC-funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health) (EP/S021930/1), the Department of Health's NIHR-funded Biomedical Research Centre at University College London, and GE Healthcare. The project leading to this paper has also received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives the support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Funding Information: This work is supported by the EPSRC‐funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health) (EP/S021930/1), the Department of Health's NIHR‐funded Biomedical Research Centre at University College London, and GE Healthcare. The project leading to this paper has also received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives the support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Publisher Copyright: {\textcopyright} 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.",
year = "2023",
month = nov,
doi = "https://doi.org/10.1002/alz.13158",
language = "English",
volume = "19",
pages = "5232--5252",
journal = "Alzheimer's and Dementia",
issn = "1552-5260",
publisher = "Elsevier Inc.",
number = "11",
}