Publication
Title
ML reconstruction from dynamic list-mode PET data using temporal splines
Author
Abstract
We implemented and evaluated a maximum likelihood optimality condition iteration algorithm (ML-OCI) to reconstruct dynamic PET data. The time activity curves (TACs) were reconstructed on a spatially segmented image. The segmented image paradigm effectively cancels out spatial reconstruction issues allowing a time domain evaluation of our method. The TACs were represented on a B-spline basis. We investigated different parameters of this basis such as order, number of basis functions and knot placing in a reconstruction task, using simulated dynamic list-mode data. We found that a higher density of basis functions allows the algorithm to follow faster changes in the TAC, however the TACs become noisier. Therefore an adaptive knot placing strategy is developed and evaluated. It allowed a more accurate reconstruction while preserving the same noise-level.
Language
English
Source (book)
Nuclear Science Symposium/Medical Imaging Conference, October 16-22, 2004, Rome, Italy
Publication
New York, N.Y. : IEEE, 2004
ISBN
0-7803-8700-7
Volume/pages
(2004), p. 3146-3150
ISI
000232002104106
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 27.03.2013
Last edited 28.05.2017