A deep learning reconstruction framework for X-ray computed tomography with incomplete data.
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the inner structures of specimens in a quantitative and nondestructive way. Limited by the implementation conditions, CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0224426 |
| _version_ | 1819123651782377472 |
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| author | Jianbing Dong Jian Fu Zhao He |
| author_facet | Jianbing Dong Jian Fu Zhao He |
| author_sort | Jianbing Dong |
| collection | DOAJ |
| description | As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the inner structures of specimens in a quantitative and nondestructive way. Limited by the implementation conditions, CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a deep learning reconstruction framework for incomplete data CT. It is the tight coupling of the deep learning U-net and CT reconstruction algorithm in the domain of the projection sinograms. The U-net estimated results are not the artifacts caused by the incomplete data, but the complete projection sinograms. After training, this framework is determined and can reconstruct the final high quality CT image from a given incomplete projection sinogram. Taking the sparse-view and limited-angle CT as examples, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with CT reconstruction, this framework naturally encapsulates the physical imaging model of CT systems and is easy to be extended to deal with other challenges. This work is helpful to push the application of the state-of-the-art deep learning techniques in the field of CT. |
| first_indexed | 2024-12-22T07:11:45Z |
| format | Article |
| id | doaj.art-40159f8952fe42618e131e5ae2bd7406 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T07:11:45Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-40159f8952fe42618e131e5ae2bd74062022-12-21T18:34:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011411e022442610.1371/journal.pone.0224426A deep learning reconstruction framework for X-ray computed tomography with incomplete data.Jianbing DongJian FuZhao HeAs a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the inner structures of specimens in a quantitative and nondestructive way. Limited by the implementation conditions, CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a deep learning reconstruction framework for incomplete data CT. It is the tight coupling of the deep learning U-net and CT reconstruction algorithm in the domain of the projection sinograms. The U-net estimated results are not the artifacts caused by the incomplete data, but the complete projection sinograms. After training, this framework is determined and can reconstruct the final high quality CT image from a given incomplete projection sinogram. Taking the sparse-view and limited-angle CT as examples, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with CT reconstruction, this framework naturally encapsulates the physical imaging model of CT systems and is easy to be extended to deal with other challenges. This work is helpful to push the application of the state-of-the-art deep learning techniques in the field of CT.https://doi.org/10.1371/journal.pone.0224426 |
| spellingShingle | Jianbing Dong Jian Fu Zhao He A deep learning reconstruction framework for X-ray computed tomography with incomplete data. PLoS ONE |
| title | A deep learning reconstruction framework for X-ray computed tomography with incomplete data. |
| title_full | A deep learning reconstruction framework for X-ray computed tomography with incomplete data. |
| title_fullStr | A deep learning reconstruction framework for X-ray computed tomography with incomplete data. |
| title_full_unstemmed | A deep learning reconstruction framework for X-ray computed tomography with incomplete data. |
| title_short | A deep learning reconstruction framework for X-ray computed tomography with incomplete data. |
| title_sort | deep learning reconstruction framework for x ray computed tomography with incomplete data |
| url | https://doi.org/10.1371/journal.pone.0224426 |
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