Learning low‐dose CT degradation from unpaired data with flow‐based model
Background There has been growing interest in low‐dose computed tomography (LDCT) for
reducing the X‐ray radiation to patients. However, LDCT always suffers from complex noise …
reducing the X‐ray radiation to patients. However, LDCT always suffers from complex noise …
Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning
J Wang, Y Tang, Z Wu, BMW Tsui, W Chen… - Medical …, 2023 - Wiley Online Library
Background In recent years, low‐dose computed tomography (LDCT) has played an
important role in the diagnosis CT to reduce the potential adverse effects of X‐ray radiation …
important role in the diagnosis CT to reduce the potential adverse effects of X‐ray radiation …
An unsupervised two‐step training framework for low‐dose computed tomography denoising
W Kim, J Lee, JH Choi - Medical Physics, 2024 - Wiley Online Library
Background Although low‐dose computed tomography (CT) imaging has been more widely
adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT …
adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT …
Unpaired low‐dose computed tomography image denoising using a progressive cyclical convolutional neural network
Q Li, R Li, S Li, T Wang, Y Cheng, S Zhang… - Medical …, 2024 - Wiley Online Library
Background Reducing the radiation dose from computed tomography (CT) can significantly
reduce the radiation risk to patients. However, low‐dose CT (LDCT) suffers from severe and …
reduce the radiation risk to patients. However, low‐dose CT (LDCT) suffers from severe and …
Investigation of low-dose CT image denoising using unpaired deep learning methods
Low-dose computed tomography (LDCT) is desired due to prevalence and ionizing radiation
of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain …
of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain …
Unpaired low‐dose CT denoising network based on cycle‐consistent generative adversarial network with prior image information
C Tang, J Li, L Wang, Z Li, L Jiang, A Cai… - … methods in medicine, 2019 - Wiley Online Library
The widespread application of X‐ray computed tomography (CT) in clinical diagnosis has
led to increasing public concern regarding excessive radiation dose administered to …
led to increasing public concern regarding excessive radiation dose administered to …
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 …
Self-supervised dual-domain network for low-dose CT denoising
Radiation dose reduction is one of the most important topics in the field of computed
tomography (CT). Over past years, deep learning based denoising methods have been …
tomography (CT). Over past years, deep learning based denoising methods have been …
Half2Half: deep neural network based CT image denoising without independent reference data
Reducing radiation dose of x-ray computed tomography (CT) and thereby decreasing the
potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been …
potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been …
[HTML][HTML] Parallel processing model for low-dose computed tomography image denoising
L Yao, J Wang, Z Wu, Q Du, X Yang, M Li… - Visual Computing for …, 2024 - Springer
Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial
role in reducing radiation exposure in patients. However, LDCT-reconstructed images often …
role in reducing radiation exposure in patients. However, LDCT-reconstructed images often …