Advances in X-ray imaging technology

Zhang, Hongyi; Teng, Gaojun; Cao, Bin; Xie, Jinlin; Cao, HongGuang; Lu, Yao; Li, YunXiang

doi:10.1360/ssv-2020-0158

Cited by 1 publication

(2 citation statements)

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“…In the quantization process, replacing h-Swish with Swish will not bring about a large loss of precision, and the efficiency can be increased by about 15%. The calculation equations are expressed as (2) (3)…”

Section: Mobilenet V3 Modelmentioning

confidence: 99%

“…Therefore, for many years, CT technology research has mainly focused on how to obtain high-quality images under low radiation dose conditions. Starting with ray sources, detectors, algorithms and other aspects [2], researchers carried out active explorations. Compared with the long research period and high cost of ray sources and detectors [3][4], CT imaging algorithms are low in cost and easy to implement, so they have always been one of the hot spots in CT technology research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-dose CT I mage D enoising M ethod B ased on Generative Adversarial Network

Jiao

Yang

Shi

et al. 2022

Preprint

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In order to solve the problems of artifacts and noise in low-dose computed tomography ( CT ) images in clinical medical diagnosis, an improved image denoising algorithm under the architecture of generative adversarial network ( GAN ) is proposed. First, a noise model based on Style GAN2 is constructed to estimate the real noise distribution, and the noise information similar to the real noise distribution is generated by as the experimental noise data set. Then, a network model with encoder-decoder architecture as the core based on GAN idea is constructed, and the network model is trained with the generated noise data set until it reaches the optimal value. Finally, the noise and artifacts in low-dose CT images can be removed by inputting low-dose CT images into the denoising network. The experimental results show that the constructed network model based on GAN architecture can improve the utilization rate of noise feature information and the stability of network training, remove image noise and artifacts, and the reconstructed image has rich texture and realistic visual effect.

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Section: Mobilenet V3 Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%