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Scatter Artifacts Removal Usings Using Learning-Based Method for CBCT in IGRT System
Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China..
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..
KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID. Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China..ORCID iD: 0000-0003-3779-5647
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 78031-78037Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 6, p. 78031-78037
Keywords [en]
CBCT, scatter correction, image registration, deep CNN
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:kth:diva-241235DOI: 10.1109/ACCESS.2018.2884704ISI: 000454607600001Scopus ID: 2-s2.0-85058116792OAI: oai:DiVA.org:kth-241235DiVA, id: diva2:1279993
Note

QC 20190117

Available from: 2019-01-17 Created: 2019-01-17 Last updated: 2019-01-17Bibliographically approved

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Li, Haibo

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