Open AccessDeep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose
Author(s) -
Kun Zhang,
Xiang Shi,
Shuangshuang Xie,
Jihang Sun,
Zhuoheng Liu,
Shuai Zhang,
Jiayang Song,
Wen Shen
Publication year - 2022
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims-21-936
Subject(s) - medicine , imaging phantom , image quality , image noise , nuclear medicine , iterative reconstruction , radiation dose , abdomen , radiology , intraclass correlation , mediastinum , image (mathematics) , artificial intelligence , computer science , clinical psychology , psychometrics
Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population.
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