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...

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Vydáno v:	Medical physics (Lancaster) Ročník 51; číslo 2; s. 1163 - 1177
Hlavní autoři:	Piao, Zun, Deng, Wenxin, Huang, Shuang, Lin, Guoqin, Qin, Peishan, Li, Xu, Wu, Wangjiang, Qi, Mengke, Zhou, Linghong, Li, Bin, Ma, Jianhui, Xu, Yuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 01.02.2024
Témata:	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
On-line přístup:	Získat plný text
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