Paper Info
Title
Parametric PET Image Reconstruction via Regional Spatial Bases and Pharmacokinetic Time Activity Model.
Abstract
It is known that the process of reconstruction of a Positron Emission Tomography (PET) image from sinogram data is very sensitive to measurement noises; it is still an important research topic to reconstruct PET images with high signal-to-noise ratios. In this paper, we propose a new reconstruction method for a temporal series of PET images from a temporal series of sinogram data. In the proposed method, PET images are reconstructed by minimizing the Kullback-Leibler divergence between the observed sinogram data and sinogram data derived from a parametric model of PET images. The contributions of the proposition include the following: (1) regions of targets in images are explicitly expressed using a set of spatial bases in order to ignore the noises in the background; (2) a parametric time activity model of PET images is explicitly introduced as a constraint; and (3) an algorithm for solving the optimization problem is clearly described. To demonstrate the advantages of the proposed method, quantitative evaluations are performed using both synthetic and clinical data of human brains.
Year
DOI
Venue
2017
10.3390/e19110629
ENTROPY
Keywords
Field
DocType
positron emission tomography,image reconstruction,pharmacokinetic modeling
Iterative reconstruction,Computer vision,Parametric model,Quantitative Evaluations,Parametric statistics,Positron emission tomography,Artificial intelligence,Optimization problem,Mathematics
Journal
Volume
Issue
ISSN
19
11
1099-4300
Citations 
PageRank 
References 
0
0.34
7
Authors
5
Name
Order
Citations
PageRank
Naoki Kawamura111.16
Tatsuya Yokota25212.26
Hidekata Hontani33616.27
Muneyuki Sakata423.10
Yuichi Kimura55114.32