Deep learning for low-dose CT denoising using perceptual loss and edge detection layer
M Gholizadeh-Ansari, J Alirezaie, P Babyn - Journal of digital imaging, 2020 - Springer
Low-dose CT denoising is a challenging task that has been studied by many researchers.
Some studies have used deep neural networks to improve the quality of low-dose CT …
Some studies have used deep neural networks to improve the quality of low-dose CT …
A cascaded convolutional neural network for x-ray low-dose CT image denoising
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis
reliability in low-dose computed tomography (CT). Machine learning based denoising …
reliability in low-dose computed tomography (CT). Machine learning based denoising …
Sharpness-aware low-dose CT denoising using conditional generative adversarial network
Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-
restricted applications, but the quantum noise as resulted by the insufficient number of …
restricted applications, but the quantum noise as resulted by the insufficient number of …
Image denoising for low-dose CT via convolutional dictionary learning and neural network
R Yan, Y Liu, Y Liu, L Wang, R Zhao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Removing noise and artifacts from low-dose computed tomography (LDCT) is a challenging
task, and most existing image-based algorithms tend to blur the results. To improve the …
task, and most existing image-based algorithms tend to blur the results. To improve the …
SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network
Computed tomography (CT) is a widely used screening and diagnostic tool that allows
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
Investigation of low-dose CT image denoising using unpaired deep learning methods
Low-dose computed tomography (LDCT) is desired due to prevalence and ionizing radiation
of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain …
of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain …
Half2Half: deep neural network based CT image denoising without independent reference data
Reducing radiation dose of x-ray computed tomography (CT) and thereby decreasing the
potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been …
potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been …
Residual U-net convolutional neural network architecture for low-dose CT denoising
Low-dose CT has received increasing attention in the recent years and is considered a
promising method to reduce the risk of cancer in patients. However, the reduction of the …
promising method to reduce the risk of cancer in patients. However, the reduction of the …
Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising
In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose
CT image denoising has attracted extensive attention from researchers, which has become …
CT image denoising has attracted extensive attention from researchers, which has become …
[HTML][HTML] Unpaired image denoising via Wasserstein GAN in low-dose CT image with multi-perceptual loss and fidelity loss
Z Yin, K Xia, Z He, J Zhang, S Wang, B Zu - Symmetry, 2021 - mdpi.com
The use of low-dose computed tomography (LDCT) in medical practice can effectively
reduce the radiation risk of patients, but it may increase noise and artefacts, which can …
reduce the radiation risk of patients, but it may increase noise and artefacts, which can …