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A Statistical Model for Positron Emission Tomography
Abstract
Positron emission tomography (PET)—still in its research stages—is a technique that promises to open new medical frontiers by enabling physicians to study the metabolic activity of the body in a pictorial manner. Much as in X-ray transmission tomography and other modes of computerized tomography, the quality of the reconstructed image in PET is very sensitive to the mathematical algorithm to be used for reconstruction. In this article, we tailor a mathematical model to the physics of positron emissions, and we use the model to describe the basic image reconstruction problem of PET as a standard problem in statistical estimation from incomplete data. We describe various estimation procedures, such as the maximum likelihood (ML) method (using the EM algorithm), the method of moments, and the least squares method. A computer simulation of a PET experiment is then used to demonstrate the ML and the least squares reconstructions. The main purposes of this article are to report on what we believe is an important contribution of statistics to PET and to familiarize statisticians with this exciting field that can benefit from further statistical methodologies to be developed with PET problems in mind. Thus no background in physics or previous knowledge of computerized tomography is assumed. The emphasis is on the basic PET model and the statistical methodology needed for it.
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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 [34]
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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
Linear Statistical Inference and its Applications
Rao, C. R. (1973) · Wiley series in probability and statistics · doi:10.1002/9780470316436 · Cited by 10,474
Iterative Solution of Nonlinear Equations in Several Variables
Ortega, J. M., & Rheinboldt, W. C. (2000) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719468 · Cited by 6,919
Journal of The American Statistical Association
Fienberg, S. E. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1317 · Cited by 6,289
Maximum likelihood estimation from incomplete data via the EM algorithm
Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,543
Maximum Likelihood Reconstruction for Emission Tomography
Shepp, L. A., & Vardi, Y. (1982) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.1982.4307558 · Cited by 4,429
Self-citationThe 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-citationEM reconstruction algorithms for emission and transmission tomography.
Lange, K. L., & Carson, R. E. (1984) · PubMed · Cited by 1,775
Nonlinear Programming: A Unified Approach.
Beale, E. M. L., & Zangwill, W. I. (1970) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2343701 · Cited by 1,059
Nonlinear Programming: A Unified Approach
Peterson, D. W., & Zangwill, W. I. (1972) · Econometrica · doi:10.2307/1909424 · Cited by 1,011
Research works printed but not linked: 2
- Lakshminarayanan , A. V. 1975 . “Reconstruction From Divergent Ray Data,” , State University of New York at Buffalo, Computer Science Dept. . Technical Report No. 92
- Peng , J. C. M. 1975 . “Simultaneous Estimation of the Parameters of Independent Poisson Distributions,” , Stanford University, Dept. of Statistics . Technical Report No. 78
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Iterative Non-negative Deconvolution Algorithms with Alpha-Divergence
Teng, Y., Yan, J., & Kang, Y. (2015) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2015.224 · Cited by 2
CROSS-REFERENCE MAXIMUM LIKELIHOOD ESTIMATE RECONSTRUCTION FOR POSITRON EMISSION TOMOGRAPHY
Chen, C., Lu, H. H., & HSU, Y.-P. (2001) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237201000248 · Cited by 2
Auxiliary-Function Methods in Iterative Optimization
Byrne, C. L. (2015) · Cited by 2
From CT to fMRI: Larry Shepp's Impact on Medical Imaging
Lindquist, M. A. (2016) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-041715-033514 · Cited by 2
Estimating the Ventilation-Perfusion Distribution: An Ill-Posed Integral Equation Problem
Lim, L. L.-Y., & Whitehead, J. (1992) · Biometrics · doi:10.2307/2532748 · Cited by 2
Sparse‐view statistical iterative head CT image reconstruction via joint regularization
Shangguan, H., Liu, Y., Cui, X., Bai, Y., Zhang, Q., & Gui, Z. (2016) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22151 · Cited by 2
Kakade, R., Walker, J. G., & Phillips, A. J. (2016) · Measurement Science and Technology · doi:10.1088/0957-0233/27/8/087001 · Cited by 2
Mihlin, A., & Levin, C. S. (2015) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2015.7582107 · Cited by 2
Ahmad, M., & Todd-Pokropek, A. E. (2008) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2008.4774199 · Cited by 2
Stoffels, M., Simon, S., Nikolic, P. G., Stoller, P. C., & Carstensen, J. (2017) · Applied Optics · doi:10.1364/ao.56.002007 · Cited by 2
Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET
Ryu, E. K., Ko, S., & Won, J. (2020) · SIAM Journal on Scientific Computing · doi:10.1137/18m1224003 · Cited by 2
Combined Correction and Reconstruction Methods
Benning, M., Kösters, T., & Lamare, F. (2012) · Series in medical physics and biomedical engineering · doi:10.1201/b11859-12 · Cited by 2
The effect of the nonlinearity on GCV applied to conjugate gradients in computerized tomography
Santos, R. J., & Pierro, Á. R. D. (2006) · Computational and Applied Mathematics · doi:10.1590/s1807-03022006000100006 · Cited by 2
A simple convergence proof of the ML-EM algorithm in the presence of background emission
Bertero, M., & Mol, C. D. (2022) · ANNALI DELL UNIVERSITA DI FERRARA · doi:10.1007/s11565-022-00414-9 · Cited by 2
Yadav, M., Oruganti, M. H., Naranjo, B., Zhang, S., Andonian, G., Zhuang, Y., Apsimon, Ö., Welsch, C. P., & Rosenzweig, J. B. (2025) · Physical Review Accelerators and Beams · doi:10.1103/physrevaccelbeams.28.042802 · Cited by 2
Chen, W., & Maitra, R. (2023) · Human Brain Mapping · doi:10.1002/hbm.26425 · Cited by 1
dos Santos, T. M., Reips, L., & Martínez, J. M. (2018) · Journal of Inverse and Ill-Posed Problems · doi:10.1515/jiip-2016-0058 · Cited by 2
Online Positron Emission Tomography By Online Portfolio Selection
Li, Y.-H. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053230 · Cited by 2
An inter-comparison of three spectral-deconvolution algorithms for gamma-ray spectroscopy
Meng, L. J., & Ramsden, D. (2003) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1999.845762 · Cited by 2
Minimax detection of localized signals in statistical inverse problems
Pohlmann, M., Werner, F., & Munk, A. (2023) · Information and Inference A Journal of the IMA · doi:10.1093/imaiai/iaad026 · Cited by 2
Kontaxakis, G., & Tzanakos, G. (2003) · IEEE Annual Northeast Bioengineering Conference (NEBEC) · doi:10.1109/nebc.1992.285968 · Cited by 2
Quarantine MAP reconstruction of PET/CT data using dual priors
Chan, C., Meikle, S. R., Fulton, R., Cai, T. W., & Feng, D. D. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.652898 · Cited by 2
Solving the attenuation problem in emission computed tomography using homomorphic filters
Chen, L.-S. (1990) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.52712 · Cited by 2
A minimum chi‐squared method for indirect parameter estimation from Poisson data
Mair, B. A., Rao, M., & Anderson, J. M. M. (2002) · Statistica Neerlandica · doi:10.1111/1467-9574.00191 · Cited by 2
On accelerated cross-reference maximum likelihood estimates for positron emission tomography
Lu, H., & Tseng, W.-J. (2002) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1997.670600 · Cited by 2
PET Image Reconstruction for Pharmacokinetic Modeling of 5-[18F]Fluorouracil in Liver Tumors
Bellemann, M. E., Doll, J., Brix, G., Haberkorn, U., Ostertag, H. J., Strauss, L. G., Dimitrakopoulou‐Strauss, A., & Lorenz, W. J. (1995) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-7340-6_12 · Cited by 2
Deconvolution of Quantized-Input Linear Systems: An Information-Theoretic Approach
Fosson, S. M. (2015) · PORTO@Iris · doi:10.6092/polito/porto/2624992 · Cited by 2
LOCAL LIKELIHOOD FOR INTERVAL-CENSORED AND AGGREGATED POINT PROCESS DATA
Fan, C. S. (2010) · TSpace (University of Toronto) · Cited by 2
Statistical image recovery techniques for optical imaging systems.
Sotthivirat, S. (2003) · Cited by 2
A Survey of Statistics in the Neurological Sciences with a Focus on Human Neuroimaging
Caffo, B. C., Zhao, Y., Eloyan, A., Wang, Z., Mejia, A. F., & Lindquist, M. A. (2018) · Wiley eBooks · doi:10.1002/9781118445112.stat08047 · Cited by 2
Discussion of the Papers by Atkinson, and Bates et al.
(1996) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1996.tb02069.x · Cited by 2
Image Restoration and Reconstruction
Johnson, V. E. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0658.pub3 · Cited by 2
Image Restoration and Reconstruction
Johnson, V. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0658 · Cited by 2
Constrained Maximum Entropy Methods in an Image Reconstruction Problem
Gelman, A. (1989) · Springer eBooks · doi:10.1007/978-94-015-7860-8_45 · Cited by 2
Positron analysis based on high energy X-ray source
Yang, Y., Li, T., Zhang, Y., Peng, Y., & Li, Y. (2009) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2009.5402193 · Cited by 1
Vaisbourd, Y., Choksi, R., Goodwin, A., Hoheisel, T., & Schönlieb, C. (2025) · Mathematical Programming · doi:10.1007/s10107-024-02189-7 · Cited by 1
Generalizable preconditioning strategies for MAP PET reconstruction using Poisson likelihood
Colombo, M. N., Paganoni, M., & Presotto, L. (2025) · Frontiers in Nuclear Medicine · doi:10.3389/fnume.2025.1661332 · Cited by 1
Mathematical methods in biomedical imaging
Burger, M., Jan Modersitzki, C., & Tenbrinck, D. (2014) · GAMM-Mitteilungen · doi:10.1002/gamm.201410008 · Cited by 1
EM Algorithms from a Non-stochastic Perspective
Byrne, C. L. (2015) · Springer eBooks · doi:10.1007/978-1-4939-0790-8_46 · Cited by 1
Conditional entropy maximization for PET
Mondal, P. P., & Rajau, K. (2004) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2003.1243896 · Cited by 1
Fixed point algorithm in PET reconstruction
Teng, Y., & Zhang, T. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5646933 · Cited by 1
Parallel Iterative Methods for Nonlinear Programming Problems
Chen, Z. (2010) · Advanced materials research · doi:10.4028/www.scientific.net/amr.159.105 · Cited by 1
Bayesian inference and Markov chain Monte Carlo in imaging
Higdon, D., & Bowsher, J. E. (1999) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.348550 · Cited by 1
Zimeras, S. (2013) · Advances in Computed Tomography · doi:10.4236/act.2013.22013 · Cited by 1
Statistical Method of Estimation of ESE in CBP
Srivastava, T. (2004) · American Journal of Mathematical and Management Sciences · doi:10.1080/01966324.2004.10737635 · Cited by 1
Hong, H., & Schonfeld, D. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2024574 · Cited by 1
Fisher Scoring Approach To Maximum Likelihood Estimation In Positron Emission Tomography
Tomitani, T. (2005) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1990.693603 · Cited by 1
Carroll, R. B., & Mair, B. A. (2001) · Journal of Mathematical Imaging and Vision · doi:10.1023/a:1011263332663 · Cited by 1
RECONSTRUCCIÓN DE IMAGEN EN TOMOGRAFÍA POR EMISIÓN DE POSITRONES
Kontaxakis, G., Vaquero, J. J., Santos, A., Electrónica, I., & Telecomunicación, E. (2002) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1
Chapeau‐Blondeau, F., & Jeanguillaume, C. (2013) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-013-0477-9 · Cited by 1
Signal Processing for Medical Imaging
Byrne, C. L. (2005) · Cited by 1
A multi-grid approach to ML reconstruction in PET: A fast alternative to EM-based techniques
O’Sullivan, F., & O’Suilleabhain, L. D. (2013) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2013.6829224 · Cited by 1
Influence of Rotation Increments on Imaging Performance for a Rotatory Dual-Head PET System
Meng, F., Cao, X., Cao, X., Wang, J., Li, L., Chen, X., Zhu, S., & Liang, J. (2017) · BioMed Research International · doi:10.1155/2017/8615086 · Cited by 1
Multi-angle reconstruction of energy dispersive X-ray diffraction spectra
Marticke, F., Paulus, C., Montémont, G., Michel, O., Mars, J. I., & Verger, L. (2014) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2014.8077640 · Cited by 1
Kawamura, N., Yokota, T., Hontani, H., Sakata, M., & Kimura, Y. (2017) · Entropy · doi:10.3390/e19110629 · Cited by 1
Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
Mejia, J. M., Mederos, B., Zhao, J., Ortega, L., & Gordillo, N. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/4706165 · Cited by 1
The root-Gaussian Cox process and a generalized EMS algorithm
Brown, P., & Stafford, J. E. (2021) · Spatial Statistics · doi:10.1016/j.spasta.2021.100509 · Cited by 1
Sundberg, R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108604574.009 · Cited by 1
Rabbertz, K. (2016) · Springer tracts in modern physics · doi:10.1007/978-3-319-42115-5_3 · Cited by 1
Mathematical concepts for image reconstruction in tomography
Kim, S., Khambampati, A. K., & Kim, K. Y. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823015-2.00028-5 · Cited by 1
A Quasi-Newton Primal-Dual Algorithm with Line Search
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Золотарев, С. А., & Taruat, A. T. (2024) · Russian Journal of Nondestructive Testing · doi:10.1134/s1061830924700682 · Cited by 1
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Beier, F., Bi, H., Sarrazin, C., Schmitzer, B., & Steidl, G. (2025) · Information and Inference A Journal of the IMA · doi:10.1093/imaiai/iaaf005 · Cited by 1
Qutbi, M. (2025) · Journal of Medical Physics · doi:10.4103/jmp.jmp_183_24 · Cited by 1
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Mondal, P. P., & Kanhirodan, R. (2006) · Conference proceedings - IEEE Instrumentation/Measurement Technology Conference · doi:10.1109/imtc.2005.1604391 · Cited by 1
Finite mixture model: a comparison method for nonlinear time series data
Phoong, S. Y., Ismail, M. T., Phoong, S. W., & Rahman, R. B. A. (2016) · International Journal of Computing Science and Mathematics · doi:10.1504/ijcsm.2016.078684 · Cited by 1
Image segmentation with physical noise models
(2016) · CRC Press eBooks · doi:10.4324/9781315372273-21 · Cited by 1
de Lima, C., & Helou, E. S. (2017) · Journal of Synchrotron Radiation · doi:10.1107/s1600577517015715 · Cited by 1
Somai, V., Légrády, D., & Tolnai, G. (2018) · Radiology and Oncology · doi:10.2478/raon-2018-0013 · Cited by 1
Low-Dose CT Image Reconstruction Using Complex Diffusion Regularization
Kaur, K., & Tiwari, S. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1135-2_50 · Cited by 1
Comment: Empirical Bayes Interval Estimation
Jiang, W. (2019) · Statistical Science · doi:10.1214/19-sts708 · Cited by 1
Overview of Medical Electronics for Physically Disabled
Pandey, V. K., & Paul, S. (2019) · Springer eBooks · doi:10.1007/978-981-13-3705-5_4 · Cited by 1
Least-squares and maximum-likelihood in XCT
Grewar, M., Myers, G. R., & Kingston, A. M. (2021) · Developments in X-Ray Tomography · doi:10.1117/12.2595559 · Cited by 1
A Bayesian multiscale framework for SPECT
Nowak, R. D., Kolaczyk, E. D., Lalush, D. S., & Tsui, B. M. W. (2003) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1999.842763 · Cited by 1
The radon transform: first steps
Τemme, N. Μ. (1987) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · Cited by 1
Laird, N. M. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess2379.pub2 · Cited by 1
Multiscale analysis for intensity and density estimation
Willett, R. M. (2002) · Rice Research Repository (Rice University) · Cited by 1
Empirical Bayes unfolding of elementary particle spectra at the Large Hadron ColliderPreprint
Kuusela, M., & Panaretos, V. M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.8274 · Cited by 1
On the Expectation-Maximization Unfolding with SmoothingPreprint
Volobouev, I. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.6500 · Cited by 1
A refined mathematical model for positron emission tomography
Mair, B. A., Rao, M., & Anderson, J. M. M. (2002) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1995.510482 · Cited by 1
Image Restoration and Reconstruction
Johnson, V. E. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat00051 · Cited by 1
Multicriterion cross-entropy minimization approach to positron emission tomographic imaging
Wang, Y. (2001) · Journal of the Optical Society of America A · doi:10.1364/josaa.18.001027 · Cited by 1
Neto, E. S. H., & Pierro, A. R. D. (2005) · Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI) · doi:10.1109/sibgrapi.2005.35 · Cited by 1
Convergence Rates for Logspline Tomography
Koo, J. (1998) · Journal of Multivariate Analysis · doi:10.1006/jmva.1998.1772 · Cited by 1
Ordered subsets Bayesian tomographic reconstruction using 2-D smoothing splines as priors
Lee, S. (2002) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(02)00112-8 · Cited by 1
A new reconstruction algorithm based on ordered projection subset and divided pixel subset
Sheng, J. (2006) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2005.12.001 · Cited by 1
A mathematical model incorporating the effects of detector width in 2D PET
Mair, B. A. (2000) · Inverse Problems · doi:10.1088/0266-5611/16/1/317 · Cited by 1
Maximum Likelihood Methods for Time-Resolved Imaging Through Turbid Media
Eriksson, B., & Nowak, R. D. (2006) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2006.312462 · Cited by 1
N-stage splitting for maximum penalized likelihood estimation
Latham, G. A., & Yu, S. (1996) · Journal of Statistical Computation and Simulation · doi:10.1080/00949659608811757 · Cited by 1
Lecomte, R., & Karuta, B. (2005) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1990.693600 · Cited by 1
Estimating mass and shape of domains in pet imaging
Ritov, Y. (1998) · Journal of nonparametric statistics · doi:10.1080/10485259808832753 · Cited by 1
Stochastic multi-scale selection of the stopping index in PET
Bissantz, N., Mair, B. A., & Munk, A. (2007) · Cited by 1
Image reconstruction for pet using fuzzy potential
Mondal, P. P., & Rajan, K. (2005) · Machine learning for signal processing ... · doi:10.1109/mlsp.2004.1423004 · Cited by 1
Mathematics of Signal Processing
Byrne, C. L. (2004) · Cited by 1
Image Reconstruction in Time-of-Flight Positron Emission Tomography
Chen, C. T., & Metz, C. E. (1996) · Information Processing in Medical Imaging · doi:10.1007/978-94-009-4261-5_28 · Cited by 1
Alternating Minimization, Proximal Minimization and Optimization Transfer Are EquivalentPreprint
Byrne, C. L., & Lee, A. J. S. (2015) · arXiv (Cornell University) · doi:10.13140/rg.2.1.3633.0327 · Cited by 1
Stochastic Model Based Image Analysis
Adali, T., & Wang, Y. (2002) · CRC Press eBooks · doi:10.1201/9780203908785.ch13 · Cited by 1
Choosing parameters in block-iterative or ordered-subset reconstruction algorithms
Byrne, C. L. (2005) · IEEE Symposium Conference Record Nuclear Science 2004. · doi:10.1109/nssmic.2004.1462771 · Cited by 1
Hartmann, A. (2017) · doi:10.53846/goediss-6237 · Cited by 1
Analysis of Spectral Clustering and the Lasso under Nonstandard Statistical Models
Rohe, K. (2011) · eScholarship (California Digital Library) · Cited by 1
Metadata
- Authors
- Vardi, Y., Shepp, L. A., & Kaufman, L.
- Affiliations
- AT&T (United States) (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1985-03
- Volume
- 80
- Issue
- 389
- Pages
- 8-20
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1967374611
- Cited by
- 863
- References
- 27
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 10 · 2023: 14 · 2022: 14 · 2021: 29 · 2020: 25 · 2019: 44 · 2018: 19 · 2017: 22 · 2016: 32 · 2015: 31 · 2014: 31 · 2013: 25 · 2012: 27
- FWCI
- 13.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (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
- positron emission tomography, image reconstruction, statistical models, missing data, maximum likelihood estimation, expectation-maximization algorithm, method of moments, least squares, statistical estimation
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