Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Evaluation of Two Automated Methods for PET Region of Interest Analysis

Evaluation of Two Automated Methods for PET Region of Interest Analysis Manual definition of regions of interest (ROIs) has been considered the reference standard method in PET data evaluation. The method is labor-intensive, prone to rater bias and may show low reproducibility. Automated template-based methods for ROI definition may overcome these limitations. The aim of this study was to validate the two automated methods FreeSurfer and the AAL template for definition of ROIs for the PET data analysis. PET data obtained using the radioligands [11C]AZD2184 (amyloid-β radioligand) and [11C]AZ10419369 (5-HT1B receptor radioligand) were evaluated. PET measurements acquired on one high and one lower resolution PET system were included. Outcome measures obtained using automated methods were compared to those obtained using manual ROIs, using linear regression analysis, intraclass correlation coefficients, and repeated measures ANOVA. ROIs provided by the automatic methods were larger than the manually delineated regions, which in some cases introduced biased estimates of the outcome measures. However, with the exception of the caudate, both AAL and FreeSurfer generally provided outcome measures that were in good agreement to those obtained from manually delineated ROIs, as long as the manually defined cerebellum was used as a reference region. Both AAL and FreeSurfer can be used for quantification of PET data, with similar accuracy in the estimates of outcome measures. Thus, the choice of method could be based upon necessity of fast analysis as provided by AAL, or more detailed ROIs and measures of cortical thickness as provided by FreeSurfer. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Evaluation of Two Automated Methods for PET Region of Interest Analysis

Loading next page...
 
/lp/springer-journals/evaluation-of-two-automated-methods-for-pet-region-of-interest-GaaOGyLB5p
Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-014-9233-6
pmid
24880728
Publisher site
See Article on Publisher Site

Abstract

Manual definition of regions of interest (ROIs) has been considered the reference standard method in PET data evaluation. The method is labor-intensive, prone to rater bias and may show low reproducibility. Automated template-based methods for ROI definition may overcome these limitations. The aim of this study was to validate the two automated methods FreeSurfer and the AAL template for definition of ROIs for the PET data analysis. PET data obtained using the radioligands [11C]AZD2184 (amyloid-β radioligand) and [11C]AZ10419369 (5-HT1B receptor radioligand) were evaluated. PET measurements acquired on one high and one lower resolution PET system were included. Outcome measures obtained using automated methods were compared to those obtained using manual ROIs, using linear regression analysis, intraclass correlation coefficients, and repeated measures ANOVA. ROIs provided by the automatic methods were larger than the manually delineated regions, which in some cases introduced biased estimates of the outcome measures. However, with the exception of the caudate, both AAL and FreeSurfer generally provided outcome measures that were in good agreement to those obtained from manually delineated ROIs, as long as the manually defined cerebellum was used as a reference region. Both AAL and FreeSurfer can be used for quantification of PET data, with similar accuracy in the estimates of outcome measures. Thus, the choice of method could be based upon necessity of fast analysis as provided by AAL, or more detailed ROIs and measures of cortical thickness as provided by FreeSurfer.

Journal

NeuroinformaticsSpringer Journals

Published: Jun 1, 2014

References