Paper Info
Title
Ordered spatial subsets for faster reconstruction in spect
Abstract
Iterative reconstruction is now becoming increasingly used in clinical SPECT reconstruction. Ordered subsets (OS) algorithms have been particularly attractive for accelerating the convergence in iterative reconstruction. Traditionally, the ordered subsets in an OS algorithm are formed by grouping the sinogram data according to the angular positions of the projection. In this work, we propose an alternative approach to form the ordered subsets by sub-sampling the spatial locations in the detector array so as to exploit the depth-dependent blur in the detector response. We demonstrate the proposed approach in the context of EM reconstruction and the results in our experiment show that it can indeed accelerate the convergence of EM; more importantly, it can further improve the convergence rate of traditional OS reconstruction when used together with angular ordered subsets.
Year
DOI
Venue
2014
10.1109/SSIAI.2014.6806015
SSIAI
Keywords
Field
DocType
image reconstruction,medical image processing,single photon emission computed tomography,em reconstruction,os algorithms,angular ordered subsets,clinical spect reconstruction,depth-dependent blur,detector array,iterative reconstruction,ordered spatial subsets,sinogram data,spatial location subsampling,osem,spect,ordered subsets,hafnium,acceleration
Convergence (routing),Iterative reconstruction,Computer vision,Detector array,Pattern recognition,Computer science,Rate of convergence,Acceleration,Artificial intelligence,Detector
Conference
ISSN
Citations 
PageRank 
1550-5782
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Wenyuan Qi185.56
Yongyi Yang21409140.74