By Topic

Estimating variability in functional images using a synthetic resampling approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Maitra, R. ; Stat. & Data Anal. Res. Group, Bellcore, Morristown, NJ ; O'Sullivan, F.

Functional imaging of biological parameters like in vivo tissue metabolism is made possible by positron emission tomography (PET). Many techniques, such as mixture analysis, have been suggested for extracting such images from dynamic sequences of reconstructed PET scans. Methods for assessing the variability in these functional images are of scientific interest. The nonlinearity of the methods used in the mixture analysis approach makes analytic formulae for estimating variability intractable. The usual resampling approach is infeasible because of the prohibitive computational effort in simulating a number of sinogram datasets, applying image reconstruction, and generating parametric images for each replication. Here we introduce an approach that approximates the distribution of the reconstructed PET images by a Gaussian random field and generates synthetic realizations in the imaging domain. This eliminates the reconstruction steps in generating each simulated functional image and is therefore practical. Results of experiments done to evaluate the approach on a model one-dimensional problem are very encouraging. Post-processing of the estimated variances is seen to improve the accuracy of the estimation method. Mixture analysis is used to estimate functional images; however, the suggested approach is general enough to extend to other parametric imaging methods

Published in:

Nuclear Science Symposium, 1996. Conference Record., 1996 IEEE  (Volume:3 )

Date of Conference:

2-9 Nov 1996