Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:65 |
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| Enthalten in: |
Physics in medicine and biology - 65(2020), 15 vom: 05. Aug., Seite 155010 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Qiyang [VerfasserIn] |
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| Anmerkungen: |
Date Completed 30.10.2020 Date Revised 30.10.2020 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1088/1361-6560/ab9066 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM309532809 |
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| 100 | 1 | |a Zhang, Qiyang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging |
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| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Hu, Zhanli |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Changhui |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Hairong |e verfasserin |4 aut | |
| 700 | 1 | |a Ge, Yongshuai |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Dong |e verfasserin |4 aut | |
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