Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography

Donald L. Snyder; Michael I. Miller; Lewis J. Thomas; David G. Politte

doi:10.1109/TMI.1987.4307831

Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography

Donald L. Snyder, Michael I. Miller, Lewis J. Thomas, David G. Politte

Research output: Contribution to journal › Article › peer-review

328 Scopus citations

Abstract

Images produced in emission tomography with the expectation-maximization algorithm have been observed to become more noisy and to have large distortions near edges as iterations proceed and the images converge towards the maximum-likelihood estimate. It is our conclusion that these artifacts are fundamental to reconstructions based on maximum-likelihood estimation as it has been applied usually; they are not due to the use of the expectation-maximization algorithm, which is but one numerical approach for finding the maxi-mum-likelihood estimate. In this paper, we develop a mathematical approach for suppressing both the noise and edge artifacts by modifying the maximum-likelihood approach to include constraints which the estimate must satisfy.

Original language	English
Pages (from-to)	228-238
Number of pages	11
Journal	IEEE Transactions on Medical Imaging
Volume	6
Issue number	3
DOIs	https://doi.org/10.1109/TMI.1987.4307831
State	Published - Sep 1987

Access to Document

10.1109/TMI.1987.4307831

Cite this

@article{8a409473254b416c9b07ebe9dccbf0ce,

title = "Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography",

abstract = "Images produced in emission tomography with the expectation-maximization algorithm have been observed to become more noisy and to have large distortions near edges as iterations proceed and the images converge towards the maximum-likelihood estimate. It is our conclusion that these artifacts are fundamental to reconstructions based on maximum-likelihood estimation as it has been applied usually; they are not due to the use of the expectation-maximization algorithm, which is but one numerical approach for finding the maxi-mum-likelihood estimate. In this paper, we develop a mathematical approach for suppressing both the noise and edge artifacts by modifying the maximum-likelihood approach to include constraints which the estimate must satisfy.",

author = "Snyder, {Donald L.} and Miller, {Michael I.} and Thomas, {Lewis J.} and Politte, {David G.}",

year = "1987",

month = sep,

doi = "10.1109/TMI.1987.4307831",

language = "English",

volume = "6",

pages = "228--238",

journal = "IEEE Transactions on Medical Imaging",

issn = "0278-0062",

number = "3",

}

TY - JOUR

T1 - Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography

AU - Snyder, Donald L.

AU - Miller, Michael I.

AU - Thomas, Lewis J.

AU - Politte, David G.

PY - 1987/9

Y1 - 1987/9

N2 - Images produced in emission tomography with the expectation-maximization algorithm have been observed to become more noisy and to have large distortions near edges as iterations proceed and the images converge towards the maximum-likelihood estimate. It is our conclusion that these artifacts are fundamental to reconstructions based on maximum-likelihood estimation as it has been applied usually; they are not due to the use of the expectation-maximization algorithm, which is but one numerical approach for finding the maxi-mum-likelihood estimate. In this paper, we develop a mathematical approach for suppressing both the noise and edge artifacts by modifying the maximum-likelihood approach to include constraints which the estimate must satisfy.

AB - Images produced in emission tomography with the expectation-maximization algorithm have been observed to become more noisy and to have large distortions near edges as iterations proceed and the images converge towards the maximum-likelihood estimate. It is our conclusion that these artifacts are fundamental to reconstructions based on maximum-likelihood estimation as it has been applied usually; they are not due to the use of the expectation-maximization algorithm, which is but one numerical approach for finding the maxi-mum-likelihood estimate. In this paper, we develop a mathematical approach for suppressing both the noise and edge artifacts by modifying the maximum-likelihood approach to include constraints which the estimate must satisfy.

UR - http://www.scopus.com/inward/record.url?scp=0023422273&partnerID=8YFLogxK

U2 - 10.1109/TMI.1987.4307831

DO - 10.1109/TMI.1987.4307831

M3 - Article

AN - SCOPUS:0023422273

SN - 0278-0062

VL - 6

SP - 228

EP - 238

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 3

ER -

Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography

Abstract

Access to Document

Other files and links

Fingerprint

Cite this