Structurally-sensitive multi-scale deep neural network for low-dose CT denoising
Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical
applications. At the same time, the X-ray radiation dose associated with CT scannings raises …
applications. At the same time, the X-ray radiation dose associated with CT scannings raises …
3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network
Low-dose computed tomography (LDCT) has attracted major attention in the medical
imaging field, since CT-associated X-ray radiation carries health risks for patients. The …
imaging field, since CT-associated X-ray radiation carries health risks for patients. The …
Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss
The continuous development and extensive use of computed tomography (CT) in medical
practice has raised a public concern over the associated radiation dose to the patient …
practice has raised a public concern over the associated radiation dose to the patient …
DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising
Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …
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 …
Low-dose CT image denoising using a generative adversarial network with a hybrid loss function for noise learning
Potential risk of X-ray radiation from computed tomography (CT) has been a concern of the
public. However, simply decreasing the dose will degrade quality of the CT images and …
public. However, simply decreasing the dose will degrade quality of the CT images and …
Artifact and detail attention generative adversarial networks for low-dose CT denoising
X Zhang, Z Han, H Shangguan, X Han… - … on Medical Imaging, 2021 - ieeexplore.ieee.org
Generative adversarial networks are being extensively studied for low-dose computed
tomography denoising. However, due to the similar distribution of noise, artifacts, and high …
tomography denoising. However, due to the similar distribution of noise, artifacts, and high …
Unpaired low‐dose CT denoising network based on cycle‐consistent generative adversarial network with prior image information
C Tang, J Li, L Wang, Z Li, L Jiang, A Cai… - … methods in medicine, 2019 - Wiley Online Library
The widespread application of X‐ray computed tomography (CT) in clinical diagnosis has
led to increasing public concern regarding excessive radiation dose administered to …
led to increasing public concern regarding excessive radiation dose administered to …
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 …
Low‐dose CT image denoising with improving WGAN and hybrid loss function
Z Li, W Shi, Q Xing, Y Miao, W He… - … Methods in Medicine, 2021 - Wiley Online Library
The X‐ray radiation from computed tomography (CT) brought us the potential risk. Simply
decreasing the dose makes the CT images noisy and diagnostic performance compromised …
decreasing the dose makes the CT images noisy and diagnostic performance compromised …