By Topic

Iterative nonlinear least squares algorithms for direct reconstruction of parametric images from dynamic PET

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
$33 $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)
Guobao Wang ; Department of Biomedical Engineering, University of California, Davis, 95616, U.S.A. ; Jinyi Qi

Indirect and direct methods have been developed for reconstructing parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate the parametric images directly from the dynamic PET data and are statistically more efficient, but the algorithms are often difficult to implement. This paper presents a simple, monotonically convergent iterative algorithm for direct reconstruction of parametric images. Each iteration of the proposed algorithm consists of two separate steps: reconstruction of dynamic images followed by a pixel-wise weighted nonlinear least squares fitting. This algorithm resembles the empirical iterative implementation of the indirect approach, but converges to the solution of the direct formulation.

Published in:

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

14-17 May 2008