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Proceedings Paper

An automated normative-based fluorodeoxyglucose positron emission tomography image-analysis procedure to aid Alzheimer disease diagnosis using statistical parametric mapping and interactive image display
Author(s): Kewei Chen; Xiaolin Ge; Li Yao; Dan Bandy; Gene E. Alexander; Anita Prouty; Christine Burns; Xiaojie Zhao; Xiaotong Wen; Ronald Korn; Michael Lawson; Eric M. Reiman
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Paper Abstract

Having approved fluorodeoxyglucose positron emission tomography (FDG PET) for the diagnosis of Alzheimer's disease (AD) in some patients, the Centers for Medicare and Medicaid Services suggested the need to develop and test analysis techniques to optimize diagnostic accuracy. We developed an automated computer package comparing an individual's FDG PET image to those of a group of normal volunteers. The normal control group includes FDG-PET images from 82 cognitively normal subjects, 61.89±5.67 years of age, who were characterized demographically, clinically, neuropsychologically, and by their apolipoprotein E genotype (known to be associated with a differential risk for AD). In addition, AD-affected brain regions functionally defined as based on a previous study (Alexander, et al, Am J Psychiatr, 2002) were also incorporated. Our computer package permits the user to optionally select control subjects, matching the individual patient for gender, age, and educational level. It is fully streamlined to require minimal user intervention. With one mouse click, the program runs automatically, normalizing the individual patient image, setting up a design matrix for comparing the single subject to a group of normal controls, performing the statistics, calculating the glucose reduction overlap index of the patient with the AD-affected brain regions, and displaying the findings in reference to the AD regions. In conclusion, the package automatically contrasts a single patient to a normal subject database using sound statistical procedures. With further validation, this computer package could be a valuable tool to assist physicians in decision making and communicating findings with patients and patient families.

Paper Details

Date Published: 15 March 2006
PDF: 9 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614453 (15 March 2006); doi: 10.1117/12.651069
Show Author Affiliations
Kewei Chen, Banner Good Samaritan Medical Ctr. (United States)
Arizona State Univ. (United States)
Univ. of Arizona (United States)
Xiaolin Ge, Mata Systems (United States)
Li Yao, Beijing Normal Univ. (China)
Dan Bandy, Banner Good Samaritan Medical Ctr. (United States)
Arizona Alzheimer Research Consortium (United States)
Gene E. Alexander, Arizona State Univ. (United States)
Arizona Alzheimer Research Consortium (United States)
Anita Prouty, Banner Good Samaritan Medical Ctr. (United States)
Arizona Alzheimer Research Consortium (United States)
Christine Burns, Banner Good Samaritan Medical Ctr. (United States)
Arizona Alzheimer Research Consortium (United States)
Xiaojie Zhao, Beijing Normal Univ. (China)
Xiaotong Wen, Beijing Normal Univ. (China)
Ronald Korn, Scottsdale Medical Imaging (United States)
Michael Lawson, Scottsdale Medical Imaging (United States)
Eric M. Reiman, Banner Good Samaritan Medical Ctr. (United States)
Univ. of Arizona (United States)
Translational Genomics Research Institute (United States)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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