Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study-Reference-Cited by-同舟云学术

Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study

Published:2021-08-14 Issue:2 Volume:32 Page:1247-1255
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Choi Hyunsu,Chang Won^ORCID,Kim Jong Hyo,Ahn Chulkyun,Lee Heejin,Kim Hae Young,Cho Jungheum,Lee Yoon Jin,Kim Young Hoon

Funder

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-021-08199-9.pdf

Reference32 articles.

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3. Hara AK, Paden RG, Silva AC, Kujak JL, Lawder HJ, Pavlicek W (2009) Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR Am J Roentgenol 193:764–771

4. Leipsic J, LaBounty TM, Heilbron B et al (2010) Estimated radiation dose reduction using adaptive statistical iterative reconstruction in coronary CT angiography: the ERASIR study. AJR Am J Roentgenol 195:655–660

5. Katsura M, Matsuda I, Akahane M et al (2012) Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 22:1613–1623

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