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Cited by 357

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List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET

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Parra, L., Barrett, H.H.·1998·IEEE Transactions on Medical Imaging

Abstract

Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al., this paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate. List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including more measurement coordinates provided by a given detector. Different figures of merit for the detector performance can be computed from the Fisher information matrix (FIM). This paper uses the observed FIM, which requires a single data set, thus, avoiding costly ensemble statistics. The proposed techniques are demonstrated for an idealized two-dimensional (2-D) positron emission tomography (PET) [2-D PET] detector. We compute from simulation data the improved image quality obtained by including the time of flight of the coincident quanta.

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Metadata

Authors
Parra, L., & Barrett, H. H.
Venue
IEEE Transactions on Medical Imaging
Published
1998-04
Volume
17
Issue
2
Pages
228-235
ISSN
0278-0062 (ISSN-L)
OpenAlex
W2108078594
Cited by
357
References
1
FWCI
5.09
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET. https://app.cochat.ai/papers/10.1109/42.700734
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Parra, L., & Barrett, H. H. (1998). List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/42.700734

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