Low-dose CT denoising with convolutional neural network

10/02/2016
by   Hu Chen, et al.
0

To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.

READ FULL TEXT

page 2

page 3

research
05/02/2018

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

Computed tomography (CT) is a popular medical imaging modality in clinic...
research
02/18/2021

Noise Entangled GAN For Low-Dose CT Simulation

We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose comput...
research
02/01/2017

Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

Given the potential X-ray radiation risk to the patient, low-dose CT has...
research
06/26/2018

Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography

In coronary CT angiography, a series of CT images are taken at different...
research
07/06/2022

Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising

The acquisition conditions for low-dose and high-dose CT images are usua...
research
11/26/2018

Low-Dose CT via Deep CNN with Skip Connection and Network in Network

A major challenge in computed tomography (CT) is how to minimize patient...
research
05/30/2020

Probabilistic self-learning framework for Low-dose CT Denoising

Despite the indispensable role of X-ray computed tomography (CT) in diag...

Please sign up or login with your details

Forgot password? Click here to reset