Robust iterative methods: Convergence and applications to proton computed tomography Article Swipe
Paniz Karbasi
,
Keith E. Schubert
,
Blake Schultze
,
V. Bashkirov
,
R. P. Johnson
,
R. Schulte
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1063/1.5127700
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1063/1.5127700
Robust methods, such as Tikhonov regularization and Bounded data uncertainty, have been used extensively in relatively small problems involving dense matrices for many decades, but have not been used in large-scale iterative methods for image reconstruction in particle imaging until recently. In this case, robust methods may allow more accurate reconstruction of images in the presence of errors of both the energy measurement of the protons and ions but also in the estimated path taken by the proton or ion through the object. Robust systems may also be used when entire blocks of data are missing, or in low-dose reconstructions using a very small number of particles without substantial loss of image quality. In this contribution, we demonstrate that robust methods show great promise in proton/ion (particle) computed tomography (pCT), and, for the first time, that they can be proven to converge. Thus, the convergence of robust methods as well as benefits for reconstruction in uncertain systems is shown to constitute the main advantage for pCT reconstruction.
Related Topics
Concepts
Tikhonov regularization
Iterative reconstruction
Regularization (linguistics)
Computer science
Tomography
Convergence (economics)
Algorithm
Robustness (evolution)
Mathematical optimization
Artificial intelligence
Mathematics
Inverse problem
Physics
Optics
Chemistry
Economic growth
Biochemistry
Gene
Economics
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/1.5127700
- https://aip.scitation.org/doi/pdf/10.1063/1.5127700
- OA Status
- bronze
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2978233211
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2978233211Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/1.5127700Digital Object Identifier
- Title
-
Robust iterative methods: Convergence and applications to proton computed tomographyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
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Paniz Karbasi, Keith E. Schubert, Blake Schultze, V. Bashkirov, R. P. Johnson, R. SchulteList of authors in order
- Landing page
-
https://doi.org/10.1063/1.5127700Publisher landing page
- PDF URL
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https://aip.scitation.org/doi/pdf/10.1063/1.5127700Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
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https://aip.scitation.org/doi/pdf/10.1063/1.5127700Direct OA link when available
- Concepts
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Tikhonov regularization, Iterative reconstruction, Regularization (linguistics), Computer science, Tomography, Convergence (economics), Algorithm, Robustness (evolution), Mathematical optimization, Artificial intelligence, Mathematics, Inverse problem, Physics, Optics, Chemistry, Economic growth, Biochemistry, Gene, Economics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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