The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Implementing and Accelerating the EM Algorithm for Positron Emission Tomography
Abstract
Since the publication of Shepp and Vadi's [ 14] maximum likelihood reconstruction algorithm for emission tomography (ET), many medical research centers engaged in ET have made an effort to change their reconstruction algorithms to this new approach. Some have succeeded, while others claim they could not adopt this new approach primarily because of limited computing power. In this paper, we discuss techniques for reducing the computational requirements of the reconstruction algorithm. Specifically, the paper discusses the data structures one might use and ways of taking advantage of the geometry of the physical system. The paper also treats some of the numerical aspects of the EM (expectation maximization) algorithm, and ways of speeding up the numerical algorithm using some of the traditional techniques of numerical analysis.
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Citations
Cites
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
A 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
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Herman, G. T., & Meyer, L. B. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.241889 · Cited by 486
Metadata
- Authors
- Kaufman, L.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1987-03
- Volume
- 6
- Issue
- 1
- Pages
- 37-51
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W1987649450
- Cited by
- 293
- References
- 2
- FWCI
- 9.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kaufman, L. (1987). Implementing and Accelerating the EM Algorithm for Positron Emission Tomography. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.1987.4307796
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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