The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Maximum Likelihood Reconstruction for Emission Tomography
Abstract
Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density lambda = lambda(x, y, z) generates, and is to be reconstructed from, the number of counts n(*)(d) in each of D detector units d. Within the model, we give an algorithm for determining an estimate lambdainsertion mark of lambda which maximizes the probability p(n(*)|lambda) of observing the actual detector count data n(*) over all possible densities lambda. Let independent Poisson variables n(b) with unknown means lambda(b), b = 1, ..., B represent the number of unobserved emissions in each of B boxes (pixels) partitioning an object containing an emitter. Suppose each emission in box b is detected in detector unit d with probability p(b, d), d = 1, ..., D with p(b,d) a one-step transition matrix, assumed known. We observe the total number n(*) = n(*)(d) of emissions in each detector unit d and want to estimate the unknown lambda = lambda(b), b = 1, ..., B. For each lambda, the observed data n(*) has probability or likelihood p(n(*)|lambda). The EM algorithm of mathematical statistics starts with an initial estimate lambda(0) and gives the following simple iterative procedure for obtaining a new estimate lambdainsertion mark(new), from an old estimate lambdainsertion mark(old), to obtain lambdainsertion mark(k), k = 1, 2, ..., lambdainsertion mark(new)(b)= lambdainsertion mark(old)(b)Sum of (n(*)p(b,d) from d=1 to D/Sum of lambdainsertion mark()old(b('))p(b('),d) from b(')=1 to B), b=1,...B.
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“Estimates of I maximizing Eq. 1 can be obtained with an expectation maximization algorithm (6, 33–35), but the outcome is typically unstable (36).”
From Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile (2021) · cites this paper as [33]
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The Fourier reconstruction of a head section
Shepp, L. A., & Logan, B. F. (1974) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1974.6499235 · Cited by 2,202
Self-citationVeinott, A. F., & Abadie, J. (1968) · Journal of the American Statistical Association · doi:10.2307/2283909 · Cited by 383
A Matheematical Model for Positron-Emission Tomography Systems Having Time-of-Flight Measurements
Snyder, D. L., Thomas, L. J., & Ter‐Pogossian, M. M. (1981) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1981.4332168 · Cited by 222
Emission Computer Assisted Tomography with Single-Photon and Positron Annihilation Photon Emitters
Budinger, T. F., Derenzo, S. E., Gullberg, G. T., Greenberg, W. L., & Huesman, R. H. (1977) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-197701000-00015 · Cited by 200
Ter‐Pogossian, M. M., Raichle, M. E., & Sobel, B. E. (1980) · Scientific American · doi:10.1038/scientificamerican1080-170 · Cited by 173
Relaxation methods for image reconstruction
Herman, G. T., Lent, A. H., & Lutz, P. H. (1978) · Communications of the ACM · doi:10.1145/359340.359351 · Cited by 137
Cho, Z. H., Chan, J., & Eriksson, L. (1976) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1976.4328315 · Cited by 91
Nonparametric Estimation in Renewal Processes
Vardi, Y. (1982) · The Annals of Statistics · doi:10.1214/aos/1176345870 · Cited by 70
Self-citation
Research works printed but not linked: 1
- lakshminarayanan, 1975, Reconstruction from divergent ray data
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Sorted by citations
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 14,137
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Lee, D. D., & Seung, H. S. (2000) · Cited by 7,972
On the Statistical Analysis of Dirty Pictures
Besag, J. E. (1986) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1986.tb01412.x · Cited by 4,204
Accelerated image reconstruction using ordered subsets of projection data
Hudson, H. M., & Larkin, R. S. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363108 · Cited by 3,713
Metadata
- Authors
- Shepp, L. A., & Vardi, Y.
- Affiliations
- AT&T (United States) (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1982-10
- Volume
- 1
- Issue
- 2
- Pages
- 113-122
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2069629287
- Cited by
- 4,429
- References
- 9
- Citations by year
- 2026: 74 · 2025: 101 · 2024: 122 · 2023: 101 · 2022: 117 · 2021: 140 · 2020: 154 · 2019: 153 · 2018: 137 · 2017: 142 · 2016: 152 · 2015: 164 · 2014: 167 · 2013: 182 · 2012: 176
- FWCI
- 10.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- emission tomography, maximum likelihood reconstruction, expectation-maximization algorithm, Poisson likelihood, tomographic reconstruction
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