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Authors
Zhou, Leilei
Liu, Hao
Zou, Yi-Xuan
Zhang, Guozhi
Su, Bin
Lu, Liyan
Chen, Yu-Chen https://orcid.org/0000-0002-8539-7224
Yin, Xindao
Jiang, Hong-Bing
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Funding
Funding for this research was provided by:
Jiangsu Provincial Special Program of Medical Science (BE2021604)
Xinghuo Talent Program of Nanjing First Hospital
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Article History
Received: 12 January 2022
Revised: 25 April 2022
Accepted: 13 May 2022
First Online: 9 June 2022
Declarations
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: The scientific guarantor of this publication is Yu-Chen Chen.
: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
: No complex statistical methods were necessary for this paper.
: This retrospective study was reviewed and approved by the Institutional Review Board of Nanjing First Hospital, and allowed to waive the informed consent.
: This study was approved by the Institutional Review Board of Nanjing Medical University.And the exemption of written informed consent was approved.
: Some study subjects used for model development have been previously reported in “Su, B, Wen, Y, Liu, Y, et al (2021) A deep learning method for eliminating head motion artifacts in computed tomography. Med Phys 49:411- 419.”
: • retrospective• observational study• performed at one institution
