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IMAGE QUALITY IN CONE BEAM COMPUTED TOMOGRAPHY IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORKS
专利权人:
Elekta; Inc.
发明人:
Jiaofeng Xu,Xiao Han
申请号:
US15964983
公开号:
US20180374245A1
申请日:
2018.04.27
申请国别(地区):
US
年份:
2018
代理人:
摘要:
Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.
来源网站:
中国工程科技知识中心
来源网址:
http://www.ckcest.cn/home/

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