Self-supervised physics-based denoising for computed tomography
E Zainulina, A Chernyavskiy, DV Dylov - arXiv preprint arXiv:2211.00745, 2022 - arxiv.org
Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation,
stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the …
stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the …
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 …
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 …
[HTML][HTML] Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network
Y Zhang, D Hao, Y Lin, W Sun, J Zhang… - … Imaging in Medicine …, 2023 - ncbi.nlm.nih.gov
Background Low-dose computed tomography (LDCT) scans can effectively reduce the
radiation damage to patients, but this is highly detrimental to CT image quality. Deep …
radiation damage to patients, but this is highly detrimental to CT image quality. Deep …
X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels
Q Du, Y Tang, J Wang, X Hou, Z Wu, M Li… - Computers in Biology …, 2023 - Elsevier
In clinical applications, multi-dose scan protocols will cause the noise levels of computed
tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising …
tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising …
Low-dose CT image denoising using residual convolutional network with fractional TV loss
M Chen, YF Pu, YC Bai - Neurocomputing, 2021 - Elsevier
In this work, we propose a Fractional-order Residual Convolutional Neural Network
(FRCNN) for Low-Dose CT (LDCT) denoising. As increasing the dose of radiation is harmful …
(FRCNN) for Low-Dose CT (LDCT) denoising. As increasing the dose of radiation is harmful …
Denoising computed tomography images with 3D-convolution based neural networks
S Moilanen - 2021 - oulurepo.oulu.fi
Low-dose computed tomography (CT) is an imaging technique used in imaging cross-
sectional images of the body that minimizes the radiation dose of the patient. Low-dose CT …
sectional images of the body that minimizes the radiation dose of the patient. Low-dose CT …
Limited parameter denoising for low‐dose X‐ray computed tomography using deep reinforcement learning
Background The use of deep learning has successfully solved several problems in the field
of medical imaging. Deep learning has been applied to the CT denoising problem …
of medical imaging. Deep learning has been applied to the CT denoising problem …
Design and implementation of convolutional neural network architectures for low-dose CT image noise reduction
S Badretale - rshare.library.torontomu.ca
An essential objective in low-dose Computed Tomography (CT) imaging is how best to
preserve the image quality. While the image quality lowers with reducing the X-ray dosage …
preserve the image quality. While the image quality lowers with reducing the X-ray dosage …
Unpaired image denoising using a generative adversarial network in X-ray CT
This paper proposes a deep learning-based denoising method for noisy low-dose
computerized tomography (CT) images in the absence of paired training data. The proposed …
computerized tomography (CT) images in the absence of paired training data. The proposed …