Deep convolutional framelet denosing for low-dose CT via wavelet residual network
Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography
(CT) are computationally expensive. To address this problem, we recently proposed a deep …
(CT) are computationally expensive. To address this problem, we recently proposed a deep …
Wavelet domain residual network (WavResNet) for low-dose X-ray CT reconstruction
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are
computationally complex because of the repeated use of the forward and backward …
computationally complex because of the repeated use of the forward and backward …
A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction
Purpose Due to the potential risk of inducing cancer, radiation exposure by X‐ray CT
devices should be reduced for routine patient scanning. However, in low‐dose X‐ray CT …
devices should be reduced for routine patient scanning. However, in low‐dose X‐ray CT …
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 …
Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …
promising performance on low‐dose CT imaging in recent years. However, most existing …
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 …
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 …
Low-dose CT restoration via stacked sparse denoising autoencoders
Y Liu, Y Zhang - Neurocomputing, 2018 - Elsevier
To improve the imaging quality of low-dose computed tomography (CT) images, a deep
learning based method for low-dose CT restoration is presented in this paper. Stacked …
learning based method for low-dose CT restoration is presented in this paper. Stacked …
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 …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …