Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy
Purpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT pro...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.00242/full |
| _version_ | 1818994620243116032 |
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| author | James J. Sohn Changsoo Kim Dong Hyun Kim Seu-Ran Lee Jun Zhou Xiaofeng Yang Tian Liu |
| author_facet | James J. Sohn Changsoo Kim Dong Hyun Kim Seu-Ran Lee Jun Zhou Xiaofeng Yang Tian Liu |
| author_sort | James J. Sohn |
| collection | DOAJ |
| description | Purpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT projection with the non-local total variation (NLTV) method.Methods: We modeled the low-dose CBCT reconstruction as recovering high quality, high-dose CBCT x-ray projections (100 kVp, 1.6 mAs) from low-dose, noisy CBCT x-ray projections (100 kVp, 0.1 mAs). The restoration of CBCT projections was performed using the NLTV regularization method. In NLTV, the x-ray image is optimized by minimizing an energy function that penalizes gray-level difference between pair of pixels between noisy x-ray projection and denoising x-ray projection. After the noisy projection is restored by NLTV regularization, the standard FDK method was applied to generate the final reconstruction output.Results: Significant noise reduction was achieved comparing to original, noisy inputs while maintaining the image quality comparable to the high-dose CBCT projections. The experimental validations show the proposed NLTV algorithm can robustly restore the noise level of x-ray projection images while significantly improving the overall image quality. The improvement in normalized mean square error (NMSE) and peak signal-to-noise ratio (PSNR) measured from the non-local total variation-gradient projection (NLTV-GPSR) algorithm is noticeable compared to that of uncorrected low-dose CBCT images. Moreover, the difference of CNRs from the gains from the proposed algorithm is noticeable and comparable to high-dose CBCT.Conclusion: The proposed method successfully restores noise degraded, low-dose CBCT projections to high-dose projection quality. Such an outcome is a considerable improvement to the reconstruction result compared to the FDK-based method. In addition, a significant reduction in reconstruction time makes the proposed algorithm more attractive. This demonstrates the potential use of the proposed algorithm for clinical practice in radiotherapy. |
| first_indexed | 2024-12-20T21:00:50Z |
| format | Article |
| id | doaj.art-1128698e4dc5411e8486bdceca79cf5a |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-20T21:00:50Z |
| publishDate | 2020-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-1128698e4dc5411e8486bdceca79cf5a2022-12-21T19:26:42ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-02-011010.3389/fonc.2020.00242499342Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation TherapyJames J. Sohn0Changsoo Kim1Dong Hyun Kim2Seu-Ran Lee3Jun Zhou4Xiaofeng Yang5Tian Liu6Department of Radiation Oncology, Emory University, Atlanta, GeorgiaDepartment of Radiological Science, The Catholic University of Pusan, Busan, South KoreaDepartment of Radiological Science, The Catholic University of Pusan, Busan, South KoreaDepartment of Biomedical Engineering, The Catholic University of Korea, Seoul, South KoreaDepartment of Radiation Oncology, Emory University, Atlanta, GeorgiaDepartment of Radiation Oncology, Emory University, Atlanta, GeorgiaDepartment of Radiation Oncology, Emory University, Atlanta, GeorgiaPurpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT projection with the non-local total variation (NLTV) method.Methods: We modeled the low-dose CBCT reconstruction as recovering high quality, high-dose CBCT x-ray projections (100 kVp, 1.6 mAs) from low-dose, noisy CBCT x-ray projections (100 kVp, 0.1 mAs). The restoration of CBCT projections was performed using the NLTV regularization method. In NLTV, the x-ray image is optimized by minimizing an energy function that penalizes gray-level difference between pair of pixels between noisy x-ray projection and denoising x-ray projection. After the noisy projection is restored by NLTV regularization, the standard FDK method was applied to generate the final reconstruction output.Results: Significant noise reduction was achieved comparing to original, noisy inputs while maintaining the image quality comparable to the high-dose CBCT projections. The experimental validations show the proposed NLTV algorithm can robustly restore the noise level of x-ray projection images while significantly improving the overall image quality. The improvement in normalized mean square error (NMSE) and peak signal-to-noise ratio (PSNR) measured from the non-local total variation-gradient projection (NLTV-GPSR) algorithm is noticeable compared to that of uncorrected low-dose CBCT images. Moreover, the difference of CNRs from the gains from the proposed algorithm is noticeable and comparable to high-dose CBCT.Conclusion: The proposed method successfully restores noise degraded, low-dose CBCT projections to high-dose projection quality. Such an outcome is a considerable improvement to the reconstruction result compared to the FDK-based method. In addition, a significant reduction in reconstruction time makes the proposed algorithm more attractive. This demonstrates the potential use of the proposed algorithm for clinical practice in radiotherapy.https://www.frontiersin.org/article/10.3389/fonc.2020.00242/fulllow-dose CBCTnon-local total variationcompressed sensingimage reconstructionimage-guided radiation therapy (IGRT) |
| spellingShingle | James J. Sohn Changsoo Kim Dong Hyun Kim Seu-Ran Lee Jun Zhou Xiaofeng Yang Tian Liu Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy Frontiers in Oncology low-dose CBCT non-local total variation compressed sensing image reconstruction image-guided radiation therapy (IGRT) |
| title | Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy |
| title_full | Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy |
| title_fullStr | Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy |
| title_full_unstemmed | Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy |
| title_short | Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy |
| title_sort | analytical low dose cbct reconstruction using non local total variation regularization for image guided radiation therapy |
| topic | low-dose CBCT non-local total variation compressed sensing image reconstruction image-guided radiation therapy (IGRT) |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.00242/full |
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