Paper Info
Title
Kernel-Based Reconstruction Of C-11-Hydroxyephedrine Cardiac Pet Images Of The Sympathetic Nervous System
Abstract
Image reconstruction for positron emission tomography (PET) can be challenging and the resulting image typically has high noise. The kernel-based reconstruction method Ill, incorporates prior anatomic information in the reconstruction algorithm to reduce noise while preserving resolution. Prior information is incorporated in the reconstruction algorithm by means of spatial kernels originally used in machine learning. In this paper, the kernel-based method is used to reconstruct PET images of sympathetic innervation in the heart. The resulting images are compared with standard Ordered Subset Expectation Maximization (OSEM) reconstructed images qualitatively and quantitatively using data from 6 human subjects. The kernel-based method demonstrated superior SNR with preserved contrast and accuracy compared to OSEM.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856752
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Iterative reconstruction,Kernel (linear algebra),Computer vision,Computer science,Signal-to-noise ratio,Filter (signal processing),Reconstruction algorithm,Artificial intelligence,Positron emission tomography,Ordered subset expectation maximization,Cardiac PET
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Zahra Ashouri100.34
Chad R. Hunter200.34
Benjamin A. Spencer300.34
Guobao Wang48612.68
Richard M. Dansereau59014.53
Robert A. deKemp611.64