scholarly journals Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 126
Author(s):  
Zhixian Yin ◽  
Kewen Xia ◽  
Ziping He ◽  
Jiangnan Zhang ◽  
Sijie Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Denoising ◽  
Low Dose ◽  
Radiation Risk ◽  
Semantic Features ◽  
Original Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
The Difference

The use of low-dose computed tomography (LDCT) in medical practice can effectively reduce the radiation risk of patients, but it may increase noise and artefacts, which can compromise diagnostic information. The methods based on deep learning can effectively improve image quality, but most of them use a training set of aligned image pairs, which are difficult to obtain in practice. In order to solve this problem, on the basis of the Wasserstein generative adversarial network (GAN) framework, we propose a generative adversarial network combining multi-perceptual loss and fidelity loss. Multi-perceptual loss uses the high-level semantic features of the image to achieve the purpose of noise suppression by minimizing the difference between the LDCT image and the normal-dose computed tomography (NDCT) image in the feature space. In addition, L2 loss is used to calculate the loss between the generated image and the original image to constrain the difference between the denoised image and the original image, so as to ensure that the image generated by the network using the unpaired images is not distorted. Experiments show that the proposed method performs comparably to the current deep learning methods which utilize paired image for image denoising.

scholarly journals Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

2018 ◽  
Vol 37 (6) ◽  
pp. 1348-1357 ◽  
Author(s):  
Qingsong Yang ◽  
Pingkun Yan ◽  
Yanbo Zhang ◽  
Hengyong Yu ◽  
Yongyi Shi ◽  
...  
Keyword(s):  
Image Denoising ◽  
Low Dose ◽  
Wasserstein Distance ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network

scholarly journals Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Keisuke Usui ◽  
Koichi Ogawa ◽  
Masami Goto ◽  
Yasuaki Sakano ◽  
Shinsuke Kyougoku ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Dose Reduction ◽  
Image Denoising ◽  
Low Dose ◽  
Radiation Risk ◽  
Natural Image ◽  
Denoising Method ◽  
Low Dose Ct ◽  
Dose Levels

AbstractTo minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with convolutional neural networks (CNNs) have been proposed for natural image denoising; however, these approaches might introduce image blurring or loss of original gradients. The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. An abdominal CT of 100 images obtained from a public database was adopted, and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. To evaluate the image quality, image similarities determined by the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were calculated for the denoised images. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. Moreover, the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10% from that of the original method. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.

scholarly journals High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 930-943 ◽  
Author(s):  
Linlin Yang ◽  
Hong Shangguan ◽  
Xiong Zhang ◽  
Anhong Wang ◽  
Zefang Han
Keyword(s):  
Image Denoising ◽  
High Frequency ◽  
Low Dose ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network

scholarly journals Image Anonymization using Deep Convolutional Generative Adversarial Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012012
Author(s):  
K Nitalaksheswara Rao ◽  
P Jayasree ◽  
Ch.V.Murali Krishna ◽  
K Sai Prasanth ◽  
Ch Satyananda Reddy
Keyword(s):  
Deep Learning ◽  
Data Privacy ◽  
Input Image ◽  
Space Representation ◽  
Synthetic Image ◽  
Original Image ◽  
Generative Adversarial Network ◽  
Model Inversion ◽  
Adversarial Network ◽  
Recent Developments

Abstract Advancement in deep learning requires significantly huge amount of data for training purpose, where protection of individual data plays a key role in data privacy and publication. Recent developments in deep learning demonstarte a huge challenge for traditionally used approch for image anonymization, such as model inversion attack, where adversary repeatedly query the model, inorder to reconstrut the original image from the anonymized image. In order to apply more protection on image anonymization, an approach is presented here to convert the input (raw) image into a new synthetic image by applying optimized noise to the latent space representation (LSR) of the original image. The synthetic image is anonymized by adding well designed noise calculated over the gradient during the learning process, where the resultant image is both realistic and immune to model inversion attack. More presicely, we extend the approach proposed by T. Kim and J. Yang, 2019 by using Deep Convolutional Generative Adversarial Network (DCGAN) in order to make the approach more efficient. Our aim is to improve the efficiency of the model by changing the loss function to achieve optimal privacy in less time and computation. Finally, the proposed approach is demonstrated using a benchmark dataset. The experimental study presents that the proposed method can efficiently convert the input image into another synthetic image which is of high quality as well as immune to model inversion attack.

scholarly journals A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

2021 ◽  
Author(s):  
Song Xue ◽  
Rui Guo ◽  
Karl Peter Bohn ◽  
Jared Matzke ◽  
Marco Viscione ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fdg Pet ◽  
Low Dose ◽  
Standard Dose ◽  
Similarity Index ◽  
Structural Similarity ◽  
Clinical Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Learning Development

Abstract Purpose A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. Methods Brain [18F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [18F]FDG PET images of 45 patients scanned with three different scanners, [18F]FET PET images of 18 patients scanned with two different scanners, as well as [18F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. Results The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = −0.71, p < 0.05) and normalized dose acquisition (r = −0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). Conclusion The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.

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