A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT

A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT

PurposeIn recent years, cone-beam computed tomography (CBCT) is increasingly used in adaptive radiation therapy (ART). However, compared with planning computed tomography (PCT), CBCT image has much more noise and imaging artifacts. Therefore, it is necessary to improve the image quality and HU accur...

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Bibliographic Details
Main Authors: Guoya Dong, Chenglong Zhang, Xiaokun Liang, Lei Deng, Yulin Zhu, Xuanyu Zhu, Xuanru Zhou, Liming Song, Xiang Zhao, Yaoqin Xie
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Oncology
Subjects:
cone beam computed tomography
planning computed tomography
scatter correction
unsupervised deep learning
radiation oncology
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.686875/full

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https://www.frontiersin.org/articles/10.3389/fonc.2021.686875/full

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