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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]
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Quantitative cardiac SPECT reconstruction with reduced image degradation due to patient anatomy
Tsui, B. M. W., Zhao, X., Gregoriou, G. K., Lalush, D. S., Frey, E. C., Johnston, R. E., & McCartney, W. H. (1994) · IEEE Transactions on Nuclear Science · doi:10.1109/23.340655 · Cited by 147
Optimization of the acquisition geometry in digital tomosynthesis of the breast
Sechopoulos, I., & Ghetti, C. (2009) · Medical Physics · doi:10.1118/1.3090889 · Cited by 146
Inverse problems: From regularization to Bayesian inference
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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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