Publication
Title
Adaptive spatially variant optimization of a temporal spline basis for dynamic PET reconstruction
Author
Abstract
In this paper we try to find an optimal temporal basis to reconstruct dynamic list-mode PET data. The goal of this method is to avoid over-fitting of and to avoid the introduction of too much bias in the reconstructed Time Activity Curves (TACs). The optimal basis is estimated from the list-mode data during reconstruction. In particular we have evaluated the Akaike Information Criterion (AIC) as objective function for the determination of the optimal number of temporal B-spline basis functions. The optimal number is spatially variant and is determined for different physiological regions in the image. The required segmentation based on the image sequences clusters the pixels of different physiological regions. We have evaluated different clustering methods. Numerical experiments of simulated cardiac PET data showed that the method based on an initial Non-negative Matrix Factorization (NMF) which includes a Poissonian noise model performed the best. Our results show that the AIC based determination of the optimal number of basis functions was useful for dynamic PET reconstruction, especially for an accurate reconstruction of the blood input curves.
Language
English
Source (book)
15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors/IEEE Nuclear Science Symposium, October 29-November 04, 2006, San Diego, Calif.
Publication
New York, N.Y. : IEEE, 2006
Volume/pages
(2006), p. 3397-3403
ISI
000288875603101
Number
978-1-4244-0561-9
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 27.03.2013
Last edited 19.07.2017