Artifact suppression for breast specimen imaging in micro CBCT using deep learning
Abstract Background Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-024-01216-5 |
| _version_ | 1797272775702872064 |
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| author | Sorapong Aootaphao Puttisak Puttawibul Pairash Thajchayapong Saowapak S. Thongvigitmanee |
| author_facet | Sorapong Aootaphao Puttisak Puttawibul Pairash Thajchayapong Saowapak S. Thongvigitmanee |
| author_sort | Sorapong Aootaphao |
| collection | DOAJ |
| description | Abstract Background Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT. Methods In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results. Results The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed. method. Conclusions Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use. |
| first_indexed | 2024-03-07T14:34:45Z |
| format | Article |
| id | doaj.art-a0693a49198a4b6ca8567f2d1336c31b |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-07T14:34:45Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-a0693a49198a4b6ca8567f2d1336c31b2024-03-05T20:43:50ZengBMCBMC Medical Imaging1471-23422024-02-0124111410.1186/s12880-024-01216-5Artifact suppression for breast specimen imaging in micro CBCT using deep learningSorapong Aootaphao0Puttisak Puttawibul1Pairash Thajchayapong2Saowapak S. Thongvigitmanee3Faculty of Medicine, Prince of Songkla UniversityFaculty of Medicine, Prince of Songkla UniversityNational Science and Technology Development AgencyMedical Imaging System Research Team, Assistive Technology and Medical Devices Research Group, National Electronics and Computer Technology Center, National Science and Technology Development AgencyAbstract Background Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT. Methods In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results. Results The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed. method. Conclusions Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.https://doi.org/10.1186/s12880-024-01216-5Cone-beam CTIterative reconstructionScattering radiationMetal artifactTruncation artifactSparse-view sinogram |
| spellingShingle | Sorapong Aootaphao Puttisak Puttawibul Pairash Thajchayapong Saowapak S. Thongvigitmanee Artifact suppression for breast specimen imaging in micro CBCT using deep learning BMC Medical Imaging Cone-beam CT Iterative reconstruction Scattering radiation Metal artifact Truncation artifact Sparse-view sinogram |
| title | Artifact suppression for breast specimen imaging in micro CBCT using deep learning |
| title_full | Artifact suppression for breast specimen imaging in micro CBCT using deep learning |
| title_fullStr | Artifact suppression for breast specimen imaging in micro CBCT using deep learning |
| title_full_unstemmed | Artifact suppression for breast specimen imaging in micro CBCT using deep learning |
| title_short | Artifact suppression for breast specimen imaging in micro CBCT using deep learning |
| title_sort | artifact suppression for breast specimen imaging in micro cbct using deep learning |
| topic | Cone-beam CT Iterative reconstruction Scattering radiation Metal artifact Truncation artifact Sparse-view sinogram |
| url | https://doi.org/10.1186/s12880-024-01216-5 |
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