By Topic

A nonlinear variational method for improved quantification of myocardial blood flow using dynamic H215O 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

5 Author(s)
Martin Benning ; Institute for Computational and Applied Mathematics, University of Münster, 48149 Germany ; Thomas Kosters ; Frank Wubbeling ; Klaus Schafers
more authors

H215O as a PET-tracer offers the opportunity to examine perfusion of blood into tissue non-invasively (cf. [1]). It features a short radioactive half-life (≈ 2 min.) and therefore adds a smaller radiation exposure to the patient in comparison to other tracers. The disadvantages arising from the short radioactive half-life are noisy, low-resolution reconstructions. Previous algorithms first reconstruct images from each dynamic H215O dataset independently, e.g. via the standard EM-algorithm (cf. [2]) or FBP. Hence, temporal correlation is neglected. The myocardial blood flow (MBF) and other important parameters, like tissue fraction, arterial and venous spillover effects are computed subsequently from these reconstructed images. Our new method interprets the direct computation of parameters as a nonlinear inverse problem. This implies the need for inversion of a nonlinear operator G(p) (with p denoting the parameters to compute), but allows to skip the process of generating noisy images. The process is schematically described in Figure 1. Therefore, our method takes into account the temporal correlation between the datasets, and not the correlation between noisy, low resolution images. The problem is transferred to a nonlinear parameter identification problem. Furthermore, regularization can be added to each parameter independently, assuring meaningful results.

Published in:

2008 IEEE Nuclear Science Symposium Conference Record

Date of Conference:

19-25 Oct. 2008