On the Effect of Relaxation in the Convergence and Quality of Statistical Image Reconstruction for Emission Tomography Using Block-Iterative Algorithms

Relaxation is widely recognized as a useful tool for providing convergence in block-iterative algorithms [1], [2], [6]. In the present article we give new results on the convergence of RAMLA (Row Action Maximum Likelihood Algorithm) [2], filling some important theoretical gaps. Furthermore, because...

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Vydáno v:XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05) s. 13 - 20
Hlavní autoři: Neto, E.S.H., De Pierro, A.R.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 2005
Témata:
Convergence
Detectors
Filling
Image quality
Image recognition
Image reconstruction
Inverse problems
Maximum likelihood detection
Positron emission tomography
Single photon emission computed tomography
ISBN:9780769523897, 0769523897
ISSN:1530-1834
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Shrnutí:Relaxation is widely recognized as a useful tool for providing convergence in block-iterative algorithms [1], [2], [6]. In the present article we give new results on the convergence of RAMLA (Row Action Maximum Likelihood Algorithm) [2], filling some important theoretical gaps. Furthermore, because RAMLA and OS-EM (Ordered Subsets - Expectation Maximization) [4] are the algorithms for statistical reconstruction currently being used in commercial emission tomography scanners, we present a comparison between them from the viewpoint of a specific imaging task. Our experiments show the importance of relaxation to improve image quality.
ISBN:9780769523897
0769523897
ISSN:1530-1834
DOI:10.1109/SIBGRAPI.2005.35