The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Iterative reconstruction techniques in emission computed tomography
Open accessAbstract
In emission tomography statistically based iterative methods can improve image quality relative to analytic image reconstruction through more accurate physical and statistical modelling of high-energy photon production and detection processes. Continued exponential improvements in computing power, coupled with the development of fast algorithms, have made routine use of iterative techniques practical, resulting in their increasing popularity in both clinical and research environments. Here we review recent progress in developing statistically based iterative techniques for emission computed tomography. We describe the different formulations of the emission image reconstruction problem and their properties. We then describe the numerical algorithms that are used for optimizing these functions and illustrate their behaviour using small scale simulations.
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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
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
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
Bayesian-Based Iterative Method of Image Restoration*
Richardson, W. H. (1972) · Journal of the Optical Society of America · doi:10.1364/josa.62.000055 · Cited by 4,160
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866 · cited 1×
“It is also interesting to note that the same update equation (32) (without ri and si) was earlier developed for restoration of astronomy images without explicit recourse to either the EM algorithm or functional substitution methodology (Richardson 1972, Lucy 1974).”
BackgroundA Statistical Model for Positron Emission Tomography
Vardi, Y., Shepp, L. A., & Kaufman, L. (1985) · Journal of the American Statistical Association · doi:10.1080/01621459.1985.10477119 · Cited by 863
Cited by
PET versus SPECT: strengths, limitations and challenges
Rahmim, A., & Zaidi, H. (2008) · Nuclear Medicine Communications · doi:10.1097/mnm.0b013e3282f3a515 · Cited by 834
STIR: software for tomographic image reconstruction release 2
Thielemans, K., Tsoumpas, C., Mustafovic, S., Beisel, T., Aguiar, P., Dikaios, N., & Jacobson, M. W. (2012) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/4/867 · Cited by 466
Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET
Rapisarda, E., Bettinardi, V, Thielemans, K., & Gilardi, M. C. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/14/012 · Cited by 142
Reynés‐Llompart, G., Gámez‐Cenzano, C., Vercher‐Conejero, J. L., Sabaté‐Llobera, A., Calvo‐Malvar, N., & Martí‐Climent, J. M. (2018) · Medical Physics · doi:10.1002/mp.12986 · Cited by 55
Moving the goalposts while scoring―the dilemma posed by new PET technologies
Rogasch, J. M. M., Boellaard, R., Pike, L., Borchmann, P., Johnson, P., Wolf, J., Barrington, S. F., & Kobe, C. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05403-2 · Cited by 20
Metadata
- Authors
- Qi, J., & Leahy, R. M.
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2006-07-12
- Volume
- 51
- Issue
- 15
- Pages
- R541-R578
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2021594562
- Cited by
- 415
- References
- 6
- FWCI
- 11.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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