Ring artifacts correction for computed tomography image using unsupervised contrastive learning Article Swipe
Tangsheng Wang
,
Xuan Liu
,
Chulong Zhang
,
Yutong He
,
Yinping Chan
,
Yaoqin Xie
,
Xiaokun Liang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acfa60
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acfa60
Objective. Computed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise image quality and impede diagnosis. To address this challenge, we propose a novel method based on dual contrast learning image style transformation network model (DCLGAN) that effectively eliminates ring artifacts from CT images while preserving texture details. Approach . Our method involves simulating ring artifacts on real CT data to generate the uncorrected CT (uCT) data and transforming them into strip artifacts. Subsequently, the DCLGAN synthetic network is applied in the polar coordinate system to remove the strip artifacts and generate a synthetic CT (sCT). We compare the uCT and sCT images to obtain a residual image, which is then filtered to extract the strip artifacts. An inverse polar transformation is performed to obtain the ring artifacts, which are subtracted from the original CT image to produce a corrected image. Main results. To validate the effectiveness of our approach, we tested it using real CT data, simulated data, and cone beam computed tomography images of the patient’s brain. The corrected CT images showed a reduction in mean absolute error by 12.36 Hounsfield units (HU), a decrease in root mean square error by 18.94 HU, an increase in peak signal-to-noise ratio by 3.53 decibels (dB), and an improvement in structural similarity index by 9.24%. Significance. These results demonstrate the efficacy of our method in eliminating ring artifacts and preserving image details, making it a valuable tool for CT imaging.
Related Topics
Concepts
Artificial intelligence
Computer science
Image quality
Hounsfield scale
Computer vision
Mean squared error
Transformation (genetics)
Pattern recognition (psychology)
Noise reduction
Residual
Image (mathematics)
Mathematics
Computed tomography
Algorithm
Medicine
Radiology
Gene
Statistics
Chemistry
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/acfa60
- OA Status
- hybrid
- Cited By
- 5
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386761542
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386761542Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1361-6560/acfa60Digital Object Identifier
- Title
-
Ring artifacts correction for computed tomography image using unsupervised contrastive learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-15Full publication date if available
- Authors
-
Tangsheng Wang, Xuan Liu, Chulong Zhang, Yutong He, Yinping Chan, Yaoqin Xie, Xiaokun LiangList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/acfa60Publisher 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/acfa60Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Image quality, Hounsfield scale, Computer vision, Mean squared error, Transformation (genetics), Pattern recognition (psychology), Noise reduction, Residual, Image (mathematics), Mathematics, Computed tomography, Algorithm, Medicine, Radiology, Gene, Statistics, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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67Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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