蜂窝状
组内相关
医学
间质性肺病
再现性
核医学
核(代数)
断层摄影术
放射科
肺
数学
内科学
统计
组合数学
作者
Yura Ahn,Sang Min Lee,Yujin Nam,Hyunna Lee,Jooae Choe,Kyung‐Hyun Do,Joon Beom Seo
标识
DOI:10.1016/j.acra.2023.06.008
摘要
The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CT and determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels.
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