(1) To compare low-contrast detectability of a deep learning–based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIRE, IMR, and FBP).
Materials and methods
Using abdominal CT images of 100 patients reconstructed via ADMIRE and FBP, we trained DLA by feeding FBP images as input and ADMIRE images as the ground truth. To measure the low-contrast detectability, the randomized repeat scans of Catphan® phantom were performed under various conditions of radiation exposures. Twelve radiologists evaluated the presence/absence of a target on a five-point confidence scale. The multi-reader multi-case area under the receiver operating characteristic curve (AUC) was calculated, and non-inferiority tests were performed. Using American College of Radiology CT accreditation phantom, contrast-to-noise ratio, target transfer function, noise magnitude, and detectability index (d’) of DLA, ADMIRE, IMR, and FBPs were computed.
The AUC of DLA in low-contrast detectability was non-inferior to that of ADMIRE (p < .001) and superior to that of FBP (p < .001). DLA improved the image quality in terms of all physical measurements compared to FBPs from both CT vendors and showed profiles of physical measurements similar to those of ADMIRE.
The low-contrast detectability of the proposed deep learning–based denoising algorithm was non-inferior to that of ADMIRE and superior to that of FBP. The DLA could successfully improve image quality compared with FBP while showing the similar physical profiles of ADMIRE.
• Low-contrast detectability in the images denoised using the deep learning algorithm was non-inferior to that in the images reconstructed using standard algorithms.
• The proposed deep learning algorithm showed similar profiles of physical measurements to advanced iterative reconstruction algorithm (ADMIRE).
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Advanced modeled iterative reconstruction
Area under the receiver operating characteristic curve
Digital imaging and communications in medicine
Deep learning–based denoising algorithm
Filtered back projection
Graphical user interface
- IMR :
Iterative model reconstruction
Model-based iterative reconstruction
Noise power spectrum
Target transfer function
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The authors sincerely thank Jaeseok Bae, MD, PhD, and Sunkyung Jeon, MD, PhD, from Seoul National University Hospital; Jeong-sub Lee, MD, PhD, from Jeju National University Hospital; Jungheum Cho, MD, Jieun Park, MD, Yongju Kim, MD, Junbum Yeo, MD, and Sunyoung Park, MD, from Seoul National University Bundang Hospital for their participation in the human observer study. The authors thank Heejin Lee from Seoul National University Bundang Hospital for her assistance with physical measurement. The authors also thank Jong-June Jeon, PhD, from the Department of Statistics of University of Seoul and Sunkyu Choi, MS, from the Medical Research Collaborating Center (MRCC) of Seoul National University Bundang Hospital for their assistance with sample size calculations and statistical analyses.
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (*MSIT) (grant number NRF-2018R1C1B6007999), by the Seoul National University Bundang Hospital Research Fund (grant number 16-2018-004), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (*MSIT) (grant number NRF-2016R1A2B3008104), and by a KAIST grant funded by Ministry of Science and ICT (grant number N11180149).
The scientific guarantor of this publication is Won Chang.
Conflict of interest
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.
Statistics and biometry
Jong-June Jeon, PhD, and Sunkyu Choi, MS, kindly provided statistical advice for this manuscript.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained
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Kim, Y., Oh, D.Y., Chang, W. et al. Deep learning–based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study. Eur Radiol 31, 8755–8764 (2021). https://doi.org/10.1007/s00330-021-07810-3