On the benefit of dual-domain denoising in a self-supervised low-dose CT setting
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging.
Numerous data-driven image denoising algorithms were proposed to restore image quality …
Numerous data-driven image denoising algorithms were proposed to restore image quality …
Task-oriented low-dose CT image denoising
The extensive use of medical CT has raised a public concern over the radiation dose to the
patient. Reducing the radiation dose leads to increased CT image noise and artifacts, which …
patient. Reducing the radiation dose leads to increased CT image noise and artifacts, which …
Unsupervised domain adaptation for low-dose computed tomography denoising
Deep neural networks have shown great improvements in low-dose computed tomography
(CT) denoising. Early deep learning-based low-dose CT denoising algorithms were …
(CT) denoising. Early deep learning-based low-dose CT denoising algorithms were …
SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network
Computed tomography (CT) is a widely used screening and diagnostic tool that allows
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
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 …
Training low dose CT denoising network without high quality reference data
J Jing, W Xia, M Hou, H Chen, Y Liu… - Physics in Medicine & …, 2022 - iopscience.iop.org
Objective. Currently, the field of low-dose CT (LDCT) denoising is dominated by supervised
learning based methods, which need perfectly registered pairs of LDCT and its …
learning based methods, which need perfectly registered pairs of LDCT and its …
Self supervised low dose computed tomography image denoising using invertible network exploiting inter slice congruence
S Bera, PK Biswas - Proceedings of the IEEE/CVF winter …, 2023 - openaccess.thecvf.com
The resurgence of deep neural networks has created an alternative pathway for low-dose
computed tomography denoising by learning a nonlinear transformation function between …
computed tomography denoising by learning a nonlinear transformation function between …
Noise Conditioned Weight Modulation for Robust and Generalizable Low Dose CT Denoising
S Bera, PK Biswas - … Conference on Medical Image Computing and …, 2023 - Springer
Deep neural networks have been extensively studied for denoising low-dose computed
tomography (LDCT) images, but some challenges related to robustness and generalization …
tomography (LDCT) images, but some challenges related to robustness and generalization …
Weakly-supervised progressive denoising with unpaired CT images
Although low-dose CT imaging has attracted a great interest due to its reduced radiation risk
to the patients, it suffers from severe and complex noise. Recent fully-supervised methods …
to the patients, it suffers from severe and complex noise. Recent fully-supervised methods …
No-reference denoising of low-dose CT projections
E Zainulina, A Chernyavskiy… - 2021 IEEE 18th …, 2021 - ieeexplore.ieee.org
Low-dose computed tomography (LDCT) became a clear trend in radiology with an
aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction …
aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction …