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A review on CT image noise and its denoising
CT imaging is widely used in medical science over the last decades. The process of CT
image reconstruction depends on many physical measurements such as radiation dose …
image reconstruction depends on many physical measurements such as radiation dose …
A review of deep learning methods for denoising of medical low-dose CT images
J Zhang, W Gong, L Ye, F Wang, Z Shangguan… - Computers in Biology …, 2024 - Elsevier
To prevent patients from being exposed to excess of radiation in CT imaging, the most
common solution is to decrease the radiation dose by reducing the X-ray, and thus the …
common solution is to decrease the radiation dose by reducing the X-ray, and thus the …
Iterative reconstruction of low-dose CT based on differential sparse
The commonly used method to reduce the dose is to reduce the tube current. The number of
photons received by the detector decreases, making the CT image obtained by analytical …
photons received by the detector decreases, making the CT image obtained by analytical …
Image denoising: The deep learning revolution and beyond—a survey paper
Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …
oldest and most studied problems in image processing. Extensive work over several …
Low-dose CT with a residual encoder-decoder convolutional neural network
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a
considerable interest in the medical imaging field. Currently, the main stream low-dose CT …
considerable interest in the medical imaging field. Currently, the main stream low-dose CT …
Generative adversarial networks for noise reduction in low-dose CT
Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural
network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low …
network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low …
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 …
CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …
starvation and electronic noise. Recently, some works have attempted to use diffusion …
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 via convolutional neural network
H Chen, Y Zhang, W Zhang, P Liao, K Li… - Biomedical optics …, 2017 - opg.optica.org
In order to reduce the potential radiation risk, low-dose CT has attracted an increasing
attention. However, simply lowering the radiation dose will significantly degrade the image …
attention. However, simply lowering the radiation dose will significantly degrade the image …