[Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning]. Article Swipe
Jiangyuan Shi
,
Ying Song
,
Guangjun Li
,
Sen Bai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7507/1001-5515.202409021
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7507/1001-5515.202409021
Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/40566788
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411711516
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411711516Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7507/1001-5515.202409021Digital Object Identifier
- Title
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[Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning].Work title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-25Full publication date if available
- Authors
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Jiangyuan Shi, Ying Song, Guangjun Li, Sen BaiList of authors in order
- Landing page
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https://pubmed.ncbi.nlm.nih.gov/40566788Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/12236203Direct OA link when available
- Concepts
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Cone beam computed tomography, Computed tomography, Image (mathematics), Cone beam ct, Image-guided radiation therapy, Artificial intelligence, Cone (formal languages), Deep learning, Computer science, Nuclear medicine, Computer vision, Radiology, Medicine, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.raw_source_name | Sheng Wu Yi Xue Gong Cheng Xue Za Zhi |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12236203 |
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| primary_location.raw_source_name | Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi |
| primary_location.landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40566788 |
| publication_date | 2025-06-25 |
| publication_year | 2025 |
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