UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at http://www.ucl.ac.uk/finance/research/post_award/post_award_contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Alzheimer's disease detection using 11C-PiB with improved partial volume effect correction
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Raniga P, Bourgeat P, Fripp J, Acosta O, Ourselin S, Rowe C, Villemagne VL, Salvado O
  • Publication date:
    22/06/2009
  • Journal:
    Progress in Biomedical Optics and Imaging - Proceedings of SPIE
  • Volume:
    7262
  • Status:
    Published
  • Print ISSN:
    1605-7422
Abstract
Despite the increasing use of 11C-PiB in research into Alzheimer's disease (AD), there are few standardized analysis procedures that have been reported or published. This is especially true with regards to partial volume effects (PVE) and partial volume correction. Due to the nature of PET physics and acquisition, PET images exhibit relatively low spatial resolution compared to other modalities, resulting in bias of quantitative results. Although previous studies have applied PVE correction techniques on 11C-PiB data, the results have not been quantitatively evaluated and compared against uncorrected data. The aim of this study is threefold. Firstly, a realistic synthetic phantom was created to quantify PVE. Secondly, MRI partial volume estimate segmentations were used to improve voxel-based PVE correction instead of using hard segmentations. Thirdly, quantification of PVE correction was evaluated on 34 subjects (AD=10, Normal Controls (NC) =24), including 12 PiB positive NC. Regional analysis was performed using the Anatomical Automatic Labeling (AAL) template, which was registered to each patient. Regions of interest were restricted to the gray matter (GM) defined by the MR segmentation. Average normalized intensity of the neocortex and selected regions were used to evaluate the discrimination power between AD and NC both with and without PVE correction. Receiver Operating Characteristic (ROC) curves were computed for the binary discrimination task. The phantom study revealed signal losses due to PVE between 10 to 40 % which were mostly recovered to within 5% after correction. Better classification was achieved after PVE correction, resulting in higher areas under ROC curves. © 2009 SPIE.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Author
Dept of Med Phys & Biomedical Eng
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by