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Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study
Journal of Applied Clinical Medical Physics ( IF 2.0 ) Pub Date : 2021-06-23 , DOI: 10.1002/acm2.13318
Takashi Shirasaka 1 , Tsukasa Kojima 1 , Yoshinori Funama 2 , Yuki Sakai 1 , Masatoshi Kondo 1 , Ryoji Mikayama 1 , Hiroshi Hamasaki 1 , Toyoyuki Kato 1 , Yasuhiro Ushijima 3 , Yoshiki Asayama 4 , Akihiro Nishie 3
Affiliation  

In an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol.

中文翻译:

基于深度学习重建的腹部超高分辨率 CT 图像质量改善:一项幻像研究

在超高分辨率 CT (U-HRCT) 中,基于深度学习的重建 (DLR) 有望在不降低空间分辨率的情况下大幅降低图像噪声。我们在假设腹部 CT 协议的情况下评估了新算法在不同辐射剂量下对图像质量的影响。
更新日期:2021-07-21
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