Deep learning–based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study

Autor: Kyeorye Lee, Kyoung Ho Lee, Jong Chul Ye, Young Hoon Kim, Eun Hee Kang, Hae Young Kim, Won Chang, Ji Hoon Park, Yoon Jin Lee, Youngjune Kim, Dong Yul Oh
Rok vydání: 2021
Předmět:
Zdroj: European Radiology. 31:8755-8764
ISSN: 1432-1084
0938-7994
DOI: 10.1007/s00330-021-07810-3
Popis: (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). 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).
Databáze: OpenAIRE
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