Academic Journal
A TV–BM3D Iterative Algorithm for VMAT-CT Reconstruction.
| Title: | A TV–BM3D Iterative Algorithm for VMAT-CT Reconstruction. |
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| Authors: | Chien, Chia-Lung, Guo, Beibei, Zhang, Rui |
| Source: | Journal of Imaging; Apr2026, Vol. 12 Issue 4, p166, 19p |
| Subject Terms: | Image reconstruction, Iterative methods (Mathematics), Image denoising, Regularization parameter, Computed tomography, Radiotherapy, Image enhancement (Imaging systems), Volumetric-modulated arc therapy |
| Abstract: | Volumetric modulated arc therapy-computed tomography (VMAT-CT), which is the CT reconstructed using the portal images collected during VMAT, can potentially be an effective onsite imaging tool. The goal of this study was to propose an iterative reconstruction algorithm that can further improve the image quality of VMAT-CT and reduce the number of failed reconstructions. An iterative algorithm combining total variation (TV) with block-matching and 3D filtering (BM3D) was proposed, addressing the L1-L2 regularization problem using the split Bregman method. We collected portal images from 67 VMAT cases including 50 phantom and 17 real-patient cases. Both Feldkamp–Davis–Kress (FDK) and TV-BM3D iterative algorithms were used to reconstruct VMAT-CT using the collected images. The preprocessing methods developed by our group previously were also used in this study. A total of 48 out of 50 phantom cases and 15 out of 17 real-patient cases were successfully reconstructed using the iterative algorithm together with image preprocessing. In contrast, 39 phantom cases and 8 patient cases could be reconstructed using the original FDK algorithm, and 44 phantom cases and 11 patient cases could be reconstructed using the FDK algorithm together with preprocessing. Compared with the FDK algorithm, the TV-BM3D iterative algorithm significantly improved the image quality of VMAT-CT at all treatment sites. To the best of our knowledge, this study is the first to develop an iterative VMAT-CT reconstruction algorithm. It can be used to reconstruct CT images locally, and is superior to FDK-based algorithms in terms of the success rate and reconstructed image quality. This strongly supports the use of VMAT-CT as a promising imaging tool for treatment monitoring and adaptive radiotherapy. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Imaging is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=2313433X&ISBN=&volume=12&issue=4&date=20260401&spage=166&pages=166-184&title=Journal of Imaging&atitle=A%20TV%E2%80%93BM3D%20Iterative%20Algorithm%20for%20VMAT-CT%20Reconstruction.&aulast=Chien%2C%20Chia-Lung&id=DOI:10.3390/jimaging12040166 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A TV–BM3D Iterative Algorithm for VMAT-CT Reconstruction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chien%2C+Chia-Lung%22">Chien, Chia-Lung</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Beibei%22">Guo, Beibei</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Rui%22">Zhang, Rui</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Imaging; Apr2026, Vol. 12 Issue 4, p166, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Regularization+parameter%22">Regularization parameter</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Radiotherapy%22">Radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Volumetric-modulated+arc+therapy%22">Volumetric-modulated arc therapy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Volumetric modulated arc therapy-computed tomography (VMAT-CT), which is the CT reconstructed using the portal images collected during VMAT, can potentially be an effective onsite imaging tool. The goal of this study was to propose an iterative reconstruction algorithm that can further improve the image quality of VMAT-CT and reduce the number of failed reconstructions. An iterative algorithm combining total variation (TV) with block-matching and 3D filtering (BM3D) was proposed, addressing the L1-L2 regularization problem using the split Bregman method. We collected portal images from 67 VMAT cases including 50 phantom and 17 real-patient cases. Both Feldkamp–Davis–Kress (FDK) and TV-BM3D iterative algorithms were used to reconstruct VMAT-CT using the collected images. The preprocessing methods developed by our group previously were also used in this study. A total of 48 out of 50 phantom cases and 15 out of 17 real-patient cases were successfully reconstructed using the iterative algorithm together with image preprocessing. In contrast, 39 phantom cases and 8 patient cases could be reconstructed using the original FDK algorithm, and 44 phantom cases and 11 patient cases could be reconstructed using the FDK algorithm together with preprocessing. Compared with the FDK algorithm, the TV-BM3D iterative algorithm significantly improved the image quality of VMAT-CT at all treatment sites. To the best of our knowledge, this study is the first to develop an iterative VMAT-CT reconstruction algorithm. It can be used to reconstruct CT images locally, and is superior to FDK-based algorithms in terms of the success rate and reconstructed image quality. This strongly supports the use of VMAT-CT as a promising imaging tool for treatment monitoring and adaptive radiotherapy. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Imaging is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/jimaging12040166 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 166 Subjects: – SubjectFull: Image reconstruction Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Image denoising Type: general – SubjectFull: Regularization parameter Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Radiotherapy Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general – SubjectFull: Volumetric-modulated arc therapy Type: general Titles: – TitleFull: A TV–BM3D Iterative Algorithm for VMAT-CT Reconstruction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chien, Chia-Lung – PersonEntity: Name: NameFull: Guo, Beibei – PersonEntity: Name: NameFull: Zhang, Rui IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2313433X Numbering: – Type: volume Value: 12 – Type: issue Value: 4 Titles: – TitleFull: Journal of Imaging Type: main |
| ResultId | 1 |