Paper Info
Title
Simultaneous PET Image Reconstruction and Feature Extraction Method using Non-negative, Smooth, and Sparse Matrix Factorization.
Abstract
Positron emission tomography ( PET) is an important imaging technique to visualize a number of functions in the brain or human body. For reconstructing PET images from the sinogram data, an inverse problem has to be solved using numerical optimizations such as expectation-maximization ( EM)-based methods. However, the standard EM method suffers from measurement noise added in the sinogram data. In this paper, we propose a new simultaneous PET image reconstruction and parts extraction method using constrained non-negative matrix factorization. In contrast that the many existing methods reconstruct a single PET image independently, we reconstruct the time-series of PET images simultaneously from the time-series of sinograms using non-negative matrix factorization. Furthermore, we impose the smoothness constraint for the temporal feature, and the exclusive LASSO-based sparseness constraint for the spatial feature for robust image reconstruction and physically meaningful feature extraction.
Year
DOI
Venue
2018
10.23919/APSIPA.2018.8659467
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Field
DocType
ISSN
Iterative reconstruction,Data modeling,Pattern recognition,Computer science,Lasso (statistics),Matrix decomposition,Signal-to-noise ratio,Feature extraction,Artificial intelligence,Inverse problem,Sparse matrix
Conference
2309-9402
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Kazuya Kawai111.36
Hidekata Hontani23616.27
Tatsuya Yokota35212.26
Muneyuki Sakata423.10
Yuichi Kimura55114.32