A new series expansion method and its application to photon-counting CT reconstruction Article Swipe
Mats Persson
,
Lin Fu
,
Peter M. Edic
,
Bruno De Man
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1117/12.2549623
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1117/12.2549623
The introduction of photon-counting detectors in x-ray computed tomography raises the question of how reconstructionalgorithms should be adapted to photon-counting measurement data. The transition from energyintegratingto photon-counting detectors introduces new eects into the data model, such as pure Poisson statisticsand increased cross talk between detector pixels, (e.g. due to charge sharing), but it is still not known indetail how these eects can be treated accurately by the reconstruction algorithm. In this work, we proposea new reconstruction method based on penalized-likelihood reconstruction that incorporates these eects. Bystarting from a simple, easily-solved reconstruction problem and adding correction terms for the additional physicaleects, we obtain a series expansion for the solution to the image reconstruction problem. This approachserves the twofold purpose of (1) yielding a new, potentially faster method of incorporating complex detectormodels in the reconstruction process and (2) providing insight into the impact of the non-ideal physical eects onthe reconstructed image. We investigate the potential for reconstructing images from simulated photon-countingenergy-resolving CT data with the new algorithm by including correction terms representing pure Poisson statisticsand interpixel cross talk; and we investigate the impact of these physical eects on the reconstructed images.Results indicate that using two correction terms gives good agreement with the converged solution, suggestingthat the new method is feasible in practice. This new approach to image reconstruction can help in developingimproved reconstruction algorithms for photon-counting CT.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/12.2549623
- https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11312/113121H/A-new-series-expansion-method-and-its-application-to-photon/10.1117/12.2549623.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3011661056
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3011661056Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1117/12.2549623Digital Object Identifier
- Title
-
A new series expansion method and its application to photon-counting CT reconstructionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-16Full publication date if available
- Authors
-
Mats Persson, Lin Fu, Peter M. Edic, Bruno De ManList of authors in order
- Landing page
-
https://doi.org/10.1117/12.2549623Publisher landing page
- PDF URL
-
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11312/113121H/A-new-series-expansion-method-and-its-application-to-photon/10.1117/12.2549623.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11312/113121H/A-new-series-expansion-method-and-its-application-to-photon/10.1117/12.2549623.pdfDirect OA link when available
- Concepts
-
Iterative reconstruction, Photon counting, Detector, Algorithm, Pixel, Computer science, Series (stratigraphy), Poisson distribution, Photon, Reconstruction algorithm, Artificial intelligence, Computer vision, Physics, Mathematics, Optics, Statistics, Paleontology, Telecommunications, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
-
11Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.is_in_top_10_percent | False |