Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner Article Swipe
Pierre‐Antoine Rodesch
,
Salim Si‐Mohamed
,
Jérôme Lesaint
,
Philippe Douek
,
Simon Rit
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ad11a3
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ad11a3
Objective . X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach . MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results . The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance . Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.
Related Topics
Concepts
Imaging phantom
Iterative reconstruction
Image quality
Scanner
Regularization (linguistics)
Algorithm
Image resolution
Computer science
Reconstruction algorithm
Artificial intelligence
Projection (relational algebra)
Computer vision
Noise (video)
Mathematics
Nuclear medicine
Image (mathematics)
Medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/ad11a3
- OA Status
- hybrid
- Cited By
- 2
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389274952
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389274952Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/ad11a3Digital Object Identifier
- Title
-
Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scannerWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-02Full publication date if available
- Authors
-
Pierre‐Antoine Rodesch, Salim Si‐Mohamed, Jérôme Lesaint, Philippe Douek, Simon RitList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/ad11a3Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6560/ad11a3Direct OA link when available
- Concepts
-
Imaging phantom, Iterative reconstruction, Image quality, Scanner, Regularization (linguistics), Algorithm, Image resolution, Computer science, Reconstruction algorithm, Artificial intelligence, Projection (relational algebra), Computer vision, Noise (video), Mathematics, Nuclear medicine, Image (mathematics), MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Physics in Medicine & Biology |
| primary_location.landing_page_url | https://doi.org/10.1088/1361-6560/ad11a3 |
| publication_date | 2023-12-02 |
| publication_year | 2023 |
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