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On the use of successive data in the ML-EM algorithm in Positron Emission Tomography

Abstract

The Maximum Likelihood-Expectation Maximization (ML-EM) algorithm is the most popular statistical reconstruction technique for Positron Emission Tomography (PET). The ML-EM algorithm is however also renowned for its long reconstruction times. An acceleration technique for this algorithm is studied in this paper. The proposed technique starts the ML-EM algorithm before the measurement process is completed. Since the reconstruction is initiated during the scan of the patient, the time elapsed before a reconstruction becomes available is reduced. Experiments with software phantoms indicate that the quality of the reconstructed image using successive data is comparable to the quality of the reconstruction with the normal ML-EM algorithm. (authors). 7 refs, 3 figs.
Authors:
Desmedt, P; Lemahieu, I [1] 
  1. University of Ghent, ELIS Department, SInt-Pietersnieuwstraat 41, B-9000 Gent, (Belgium)
Publication Date:
Dec 31, 1994
Product Type:
Conference
Report Number:
INIS-CY-0001; CONF-940530-
Reference Number:
SCA: 550601; PA: AIX-29:011304; EDB-98:045899; SN: 98001938879
Resource Relation:
Conference: MPBE `94: 1. international conference on medical physics and biomedical engineering, Nicosia (Cyprus), 3-7 May 1994; Other Information: DN: 7 refs, 3 figs.; PBD: 1994; Related Information: Is Part Of Proceedings of the international conference on medical physics and biomedical engineering. Vol. 1; Spyrou, S.; Christofides, S.; Pattichis, C.S.; Keravnou, E.; Schizas, C.N.; Christodoulides, G. [eds.]; PB: 279 p.
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; ALGORITHMS; COMPARATIVE EVALUATIONS; DATA PROCESSING; DATA-FLOW PROCESSING; EMISSION COMPUTED TOMOGRAPHY; IMAGES; METABOLISM; PATIENTS; POSITRON COMPUTED TOMOGRAPHY; QUALITY FACTOR
OSTI ID:
595666
Research Organizations:
Cyprus Association of Medical Physics and Biomedical Engineering (CAMPBE), Nicosia (Cyprus); Cyprus Univ., Nicosia (Cyprus). Dept. of Computer Science.
Country of Origin:
Cyprus
Language:
English
Other Identifying Numbers:
Other: ON: DE98605204; ISBN 9963-607-04-7; TRN: CY9700006011304
Availability:
INIS; OSTI as DE98605204
Submitting Site:
INIS
Size:
pp. 180-184
Announcement Date:

Citation Formats

Desmedt, P, and Lemahieu, I. On the use of successive data in the ML-EM algorithm in Positron Emission Tomography. Cyprus: N. p., 1994. Web.
Desmedt, P, & Lemahieu, I. On the use of successive data in the ML-EM algorithm in Positron Emission Tomography. Cyprus.
Desmedt, P, and Lemahieu, I. 1994. "On the use of successive data in the ML-EM algorithm in Positron Emission Tomography." Cyprus.
@misc{etde_595666,
title = {On the use of successive data in the ML-EM algorithm in Positron Emission Tomography}
author = {Desmedt, P, and Lemahieu, I}
abstractNote = {The Maximum Likelihood-Expectation Maximization (ML-EM) algorithm is the most popular statistical reconstruction technique for Positron Emission Tomography (PET). The ML-EM algorithm is however also renowned for its long reconstruction times. An acceleration technique for this algorithm is studied in this paper. The proposed technique starts the ML-EM algorithm before the measurement process is completed. Since the reconstruction is initiated during the scan of the patient, the time elapsed before a reconstruction becomes available is reduced. Experiments with software phantoms indicate that the quality of the reconstructed image using successive data is comparable to the quality of the reconstruction with the normal ML-EM algorithm. (authors). 7 refs, 3 figs.}
place = {Cyprus}
year = {1994}
month = {Dec}
}