Deep learning based image reconstruction algorithm for limited-angle translational computed tomography.
As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226963&type=printable |
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| author | Jiaxi Wang Jun Liang Jingye Cheng Yumeng Guo Li Zeng |
| author_facet | Jiaxi Wang Jun Liang Jingye Cheng Yumeng Guo Li Zeng |
| author_sort | Jiaxi Wang |
| collection | DOAJ |
| description | As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures. |
| format | Article |
| id | doaj-art-e9bdfac1768143a2a4474ab5364a3c8c |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e9bdfac1768143a2a4474ab5364a3c8c2025-08-20T02:55:12ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01151e022696310.1371/journal.pone.0226963Deep learning based image reconstruction algorithm for limited-angle translational computed tomography.Jiaxi WangJun LiangJingye ChengYumeng GuoLi ZengAs a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226963&type=printable |
| spellingShingle | Jiaxi Wang Jun Liang Jingye Cheng Yumeng Guo Li Zeng Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. PLoS ONE |
| title | Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. |
| title_full | Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. |
| title_fullStr | Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. |
| title_full_unstemmed | Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. |
| title_short | Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. |
| title_sort | deep learning based image reconstruction algorithm for limited angle translational computed tomography |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226963&type=printable |
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