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Scatter Artifacts R...
Scatter Artifacts Removal Usings Using Learning-Based Method for CBCT in IGRT System
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- Xie, Shipeng (author)
- Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China.
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- Yang, Chengyuan (author)
- Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China.
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- Zhang, Zijian (author)
- Cent S Univ, Dept Oncol, Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.
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- Li, Haibo (author)
- KTH,Medieteknik och interaktionsdesign, MID,Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China.
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Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China Cent S Univ, Dept Oncol, Xiangya Hosp, Changsha 410008, Hunan, Peoples R China. (creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
- 2018
- English.
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 6, s. 78031-78037
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Cone-beam-computed tomography (CBCT) has shown enormous potential in recent years, but it is limited by severe scatter artifacts. This paper proposes a scatter-correction algorithm based on a deep convolutional neural network to reduce artifacts for CBCT in an image-guided radiation therapy (IGRT) system. A two-step registration method that is essential in our algorithm is implemented to preprocess data before training. The testing result on real data acquired from the IGRT system demonstrates the ability of our approach to learn artifacts distribution. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Medieteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Media and Communication Technology (hsv//eng)
Keyword
- CBCT
- scatter correction
- image registration
- deep CNN
Publication and Content Type
- ref (subject category)
- art (subject category)
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