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Contrast Media & Molecular Imaging
Volume 2018, Article ID 3786083, 16 pages
https://doi.org/10.1155/2018/3786083
Research Article

Study of the Influence of Age in 18F-FDG PET Images Using a Data-Driven Approach and Its Evaluation in Alzheimer’s Disease

1Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
2PET Center, Huashan Hospital, Fudan University, Shanghai, China
3Department of Nuclear Medicine, Technische Universität München, Munich, Germany
4Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China

Correspondence should be addressed to Chuantao Zuo; moc.361@23522993981

Received 24 August 2017; Revised 18 November 2017; Accepted 18 December 2017; Published 8 February 2018

Academic Editor: Jie Lu

Copyright © 2018 Jiehui Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives. 18F-FDG PET scan is one of the most frequently used neural imaging scans. However, the influence of age has proven to be the greatest interfering factor for many clinical dementia diagnoses when analyzing 18F-FDG PET images, since radiologists encounter difficulties when deciding whether the abnormalities in specific regions correlate with normal aging, disease, or both. In the present paper, the authors aimed to define specific brain regions and determine an age-correction mathematical model. Methods. A data-driven approach was used based on 255 healthy subjects. Results. The inferior frontal gyrus, the left medial part and the left medial orbital part of superior frontal gyrus, the right insula, the left anterior cingulate, the left median cingulate, and paracingulate gyri, and bilateral superior temporal gyri were found to have a strong negative correlation with age. For evaluation, an age-correction model was applied to 262 healthy subjects and 50 AD subjects selected from the ADNI database, and partial correlations between SUVR mean and three clinical results were carried out before and after age correction. Conclusion. All correlation coefficients were significantly improved after the age correction. The proposed model was effective in the age correction of both healthy and AD subjects.