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
Class Conditional Entropic Prior for MRI Enhanced SPECT Reconstruction
  • Publication Type:
    Conference
  • Authors:
    Pedemonte S, Cardoso MJ, Bousse A, Panagiotou C, Kazantsev D, Arridge S, Hutton BF, Ourselin S
  • Pagination:
    3292, 3300
  • Name of conference:
    Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE
  • Conference place:
    Knoxville, TN
  • Conference start date:
    01/10/2010
  • Conference finish date:
    01/11/2010
Abstract
Maximum Likelihood Estimation can provide an accurate estimate of activity distribution for Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT), however its unconstrained application suffers from dimensional instability due to approximation of activity distribution to a grid of point processes. Correlation between the activity distribution and the underlying tissue morphology enables the use of information from an intra-subject anatomical image to improve the activity estimate. Several approaches have been proposed to include anatomical information in the process of activity estimation. Methods based on information theoretic similarity functionals are particularly appealing as they abstract from any assumption about the nature of the images. However, due to multiplicity of the similarity functional, such methods tend to discard boundary information from the anatomical image. This paper presents an extension of state of the art methods by introducing a hidden variable denoting tissue composition that conditions an entropic similarity functional. This allows one to include explicit knowledge of the MRI imaging system model, effectively introducing additional information. The proposed method provides an intrinsic edge-preserving feature, it outperforms conventional methods based on Joint Entropy in terms of bias/variance characteristics, and it does not introduce additional parameters.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers Show More
Author
Dept of Computer Science
Author
Metabolism & Experi Therapeutics
Author
Dept of Med Phys & Biomedical Eng
Author
Metabolism & Experi Therapeutics
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