Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System
With the growing technology of photon-counting detectors (PCD), spectral CT is an important topic for its potential in material differentiation. However, direct reconstruction of the detected spectrum without any compensation will lead to inaccurate results due to some non-ideal factors such as cros...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9272761/ |
| _version_ | 1818618593842036736 |
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| author | Ao Zheng Hongkai Yang Li Zhang Yuxiang Xing |
| author_facet | Ao Zheng Hongkai Yang Li Zhang Yuxiang Xing |
| author_sort | Ao Zheng |
| collection | DOAJ |
| description | With the growing technology of photon-counting detectors (PCD), spectral CT is an important topic for its potential in material differentiation. However, direct reconstruction of the detected spectrum without any compensation will lead to inaccurate results due to some non-ideal factors such as cross talk and pulse pile-up in the detectors. Conventional methods try to model these factors using calibrations and make compensations accordingly, but the results depend on the model calibration accuracy. In this paper, we proposed an Interweaving Network (WeaveNet), a novel deep learning-based monochromatic image synthesis method working in sinogram domain. Unlike previous deep learning-based methods, the WeaveNet architecture was designed based on the factor of spectrum distortion and it can solve this problem better in an intuitive way. The method was tested on a cone-beam CT (CBCT) system equipped with a PCD. After FDK reconstruction of the synthesized monochromatic projection, we evaluated the accuracy of linear attenuation coefficient, decomposition coefficient and separation angle of different materials to examine the performance of our method. This method gives more accurate results with less noise than previous methods, which demonstrates the advantages of this monochromatic image synthesis method. |
| first_indexed | 2024-12-16T17:24:04Z |
| format | Article |
| id | doaj.art-edef5138d91a4a7c8a985b8bb8d01393 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:24:04Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-edef5138d91a4a7c8a985b8bb8d013932022-12-21T22:23:07ZengIEEEIEEE Access2169-35362020-01-01821770121771010.1109/ACCESS.2020.30410789272761Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT SystemAo Zheng0https://orcid.org/0000-0003-3600-4217Hongkai Yang1Li Zhang2Yuxiang Xing3https://orcid.org/0000-0001-9723-5655Department of Engineering Physics, Tsinghua University, Beijing, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing, ChinaWith the growing technology of photon-counting detectors (PCD), spectral CT is an important topic for its potential in material differentiation. However, direct reconstruction of the detected spectrum without any compensation will lead to inaccurate results due to some non-ideal factors such as cross talk and pulse pile-up in the detectors. Conventional methods try to model these factors using calibrations and make compensations accordingly, but the results depend on the model calibration accuracy. In this paper, we proposed an Interweaving Network (WeaveNet), a novel deep learning-based monochromatic image synthesis method working in sinogram domain. Unlike previous deep learning-based methods, the WeaveNet architecture was designed based on the factor of spectrum distortion and it can solve this problem better in an intuitive way. The method was tested on a cone-beam CT (CBCT) system equipped with a PCD. After FDK reconstruction of the synthesized monochromatic projection, we evaluated the accuracy of linear attenuation coefficient, decomposition coefficient and separation angle of different materials to examine the performance of our method. This method gives more accurate results with less noise than previous methods, which demonstrates the advantages of this monochromatic image synthesis method.https://ieeexplore.ieee.org/document/9272761/Spectral CTphoton-counting detectorsmonochromatic image synthesisdeep learning |
| spellingShingle | Ao Zheng Hongkai Yang Li Zhang Yuxiang Xing Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System IEEE Access Spectral CT photon-counting detectors monochromatic image synthesis deep learning |
| title | Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System |
| title_full | Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System |
| title_fullStr | Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System |
| title_full_unstemmed | Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System |
| title_short | Interweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System |
| title_sort | interweaving network a novel monochromatic image synthesis method for a photon counting detector ct system |
| topic | Spectral CT photon-counting detectors monochromatic image synthesis deep learning |
| url | https://ieeexplore.ieee.org/document/9272761/ |
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