CT image denoising and deblurring with deep learning: current status and perspectives
This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
Application and construction of deep learning networks in medical imaging
Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned
with the development of computational models to train artificial intelligence systems. DL …
with the development of computational models to train artificial intelligence systems. DL …
Noise suppression with similarity-based self-supervised deep learning
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and
photon-counting computed tomography (CT) denoising can optimize diagnostic …
photon-counting computed tomography (CT) denoising can optimize diagnostic …
Image denoising in the deep learning era
S Izadi, D Sutton, G Hamarneh - Artificial Intelligence Review, 2023 - Springer
Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …
Deep cascade residual networks (DCRNs): optimizing an encoder–decoder convolutional neural network for low-dose CT imaging
To suppress noise and artifacts caused by the reduced radiation exposure in low-dose
computed tomography, several deep learning (DL)-based image restoration methods have …
computed tomography, several deep learning (DL)-based image restoration methods have …
Triplet cross-fusion learning for unpaired image denoising in optical coherence tomography
Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which
suffers from the speckle noise inevitably. Deep learning has proven its superior capability in …
suffers from the speckle noise inevitably. Deep learning has proven its superior capability in …
Systematic review on learning-based spectral CT
Spectral computed tomography (CT) has recently emerged as an advanced version of
medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two …
medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two …
Sparse2noise: low-dose synchrotron x-ray tomography without high-quality reference data
Background Synchrotron radiation computed tomography (SR-CT) holds promise for high-
resolution in vivo imaging. Notably, the reconstruction of SR-CT images necessitates a large …
resolution in vivo imaging. Notably, the reconstruction of SR-CT images necessitates a large …
[HTML][HTML] Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging
Z Xia, J Liu, Y Kang, Y Wang, D Hu… - Quantitative Imaging in …, 2023 - ncbi.nlm.nih.gov
Background Computed tomography (CT) imaging technology has become an indispensable
auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage …
auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage …
Noise-generating-mechanism-driven unsupervised learning for low-dose CT sinogram recovery
D Zeng, L Wang, M Geng, S Li, Y Deng… - … on Radiation and …, 2021 - ieeexplore.ieee.org
Deep learning (DL) techniques have expedited successful applications in computed
tomography (CT) imaging field and have obtained remarkable outcomes. Most of the …
tomography (CT) imaging field and have obtained remarkable outcomes. Most of the …