Adaptive scatter kernel deconvolution modeling for cone‐beam CT scatter correction via deep reinforcement learning

Background Scattering photons can seriously contaminate cone‐beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method c...

Full description

Bibliographic Details

Published in:	Medical physics (Lancaster) Vol. 51; no. 2; pp. 1163 - 1177
Main Authors:	Piao, Zun, Deng, Wenxin, Huang, Shuang, Lin, Guoqin, Qin, Peishan, Li, Xu, Wu, Wangjiang, Qi, Mengke, Zhou, Linghong, Li, Bin, Ma, Jianhui, Xu, Yuan
Format:	Journal Article
Language:	English
Published:	United States 01.02.2024
Subjects:	adaptive scatter kernel optimization cone‐beam CT scatter correction deep reinforcement learning scatter kernel deconvolution algorithm adaptive scatter kernel optimization cone-beam CT scatter correction deep reinforcement learning scatter kernel deconvolution algorithm
ISSN:	0094-2405, 2473-4209, 2473-4209
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!