Semi-supervised learned sinogram restoration network for low-dose CT image reconstruction
With the development of deep learning (DL), many deep learning (DL) based algorithms
have been widely used in the low-dose CT imaging and achieved promising reconstruction …
have been widely used in the low-dose CT imaging and achieved promising reconstruction …
LdCT-Net: low-dose CT image reconstruction strategy driven by a deep dual network
High radiation dose in CT imaging is a major concern, which could result in increased
lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining …
lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining …
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 …
Semi-supervised learning for low-dose CT image restoration with hierarchical deep generative adversarial network (HD-GAN)
In the absence of duplicate high-dose CT data, it is challenging to restore high-quality
images based on deep learning with only low-dose CT (LDCT) data. When different …
images based on deep learning with only low-dose CT (LDCT) data. When different …
SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction
Low-dose CT plays a significant role in reducing radiation risks to patients. The main
challenge is to achieve better image quality while lowering the imaging dose. In this work …
challenge is to achieve better image quality while lowering the imaging dose. In this work …
Unsupervised data fidelity enhancement network for spectral CT reconstruction
Deep learning (DL) networks show a great potential in computed tomography (CT) imaging
field. Most of them are supervised DL network greatly based on their capability and the …
field. Most of them are supervised DL network greatly based on their capability and the …
Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography
The widespread use of computed tomography (CT) in clinical practice has made the public
focus on the cumulative radiation dose delivered to patients. Low-dose CT (LDCT) reduces …
focus on the cumulative radiation dose delivered to patients. Low-dose CT (LDCT) reduces …
Noise-generating and imaging mechanism inspired implicit regularization learning network for low dose ct reconstrution
Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning
while maintaining image quality, which involves a consistent pursuit of lower incident rays …
while maintaining image quality, which involves a consistent pursuit of lower incident rays …
Low-Dose CT Reconstruction via Dual-Domain learning and controllable modulation
Existing CNN-based low-dose CT reconstruction methods focus on restoring the degraded
CT images by processing on the image domain or the raw data (sinogram) domain …
CT images by processing on the image domain or the raw data (sinogram) domain …
Real-time image reconstruction for low-dose CT using deep convolutional generative adversarial networks (GANs)
K Choi, SW Kim, JS Lim - Medical Imaging 2018: Physics of …, 2018 - spiedigitallibrary.org
This paper introduces a deep learning network that reconstructs low-dose CT images into
CT images of a high quality comparable to adaptive statistical iterative reconstruction (ASIR) …
CT images of a high quality comparable to adaptive statistical iterative reconstruction (ASIR) …