Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.
Artificial intelligence
Deep learning
Low-dose X-ray computed tomography
Self-supervised denoising algorithm
Journal
Radiological physics and technology
ISSN: 1865-0341
Titre abrégé: Radiol Phys Technol
Pays: Japan
ID NLM: 101467995
Informations de publication
Date de publication:
27 Feb 2024
27 Feb 2024
Historique:
received:
04
12
2023
accepted:
25
01
2024
revised:
11
01
2024
pubmed:
28
2
2024
medline:
28
2
2024
entrez:
27
2
2024
Statut:
aheadofprint
Résumé
This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.
Identifiants
pubmed: 38413510
doi: 10.1007/s12194-024-00786-x
pii: 10.1007/s12194-024-00786-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.
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