Decomposition iteration strategy for low-dose CT denoising

In the medical field, computed tomography (CT) is a commonly used examination method, but the radiation generated increases the risk of illness in patients. Therefore, low-dose scanning schemes have attracted attention, in which noise reduction is essential. We propose a purposeful and interpretable...

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Veröffentlicht in:Journal of X-ray science and technology Jg. 32; H. 3; S. 493
Hauptverfasser: Li, Zhiyuan, Liu, Yi, Zhang, Pengcheng, Lu, Jing, Gui, Zhiguo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 28.05.2024
Schlagworte:
Algorithms
Humans
Image Processing, Computer-Assisted - methods
Phantoms, Imaging
Radiation Dosage
Signal-To-Noise Ratio
Tomography, X-Ray Computed - methods
iteration
CNN
LDCT
decomposition
image denoising
ISSN:1095-9114, 1095-9114
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Zusammenfassung:In the medical field, computed tomography (CT) is a commonly used examination method, but the radiation generated increases the risk of illness in patients. Therefore, low-dose scanning schemes have attracted attention, in which noise reduction is essential. We propose a purposeful and interpretable decomposition iterative network (DISN) for low-dose CT denoising. This method aims to make the network design interpretable and improve the fidelity of details, rather than blindly designing or using deep CNN architecture. The experiment is trained and tested on multiple data sets. The results show that the DISN method can restore the low-dose CT image structure and improve the diagnostic performance when the image details are limited. Compared with other algorithms, DISN has better quantitative and visual performance, and has potential clinical application prospects.
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ISSN:1095-9114
1095-9114
DOI:10.3233/XST-230272