Noise2Context: Context-assisted Learning 3D Thin-layer for Low Dose CT.

@article{Zhang2021Noise2ContextCL,
  title={Noise2Context: Context-assisted Learning 3D Thin-layer for Low Dose CT.},
  author={Zhicheng Zhang and Xiaokun Liang and Wei Zhao and Lei Xing},
  journal={Medical physics},
  year={2021}
}
PURPOSE Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of X-ray radiation exposure attract more and more attention. To lower the X-ray radiation, low-dose CT (LDCT) has been widely adopted in certain scenarios, while it will induce the degradation of CT image quality. In this paper, we proposed a deep learning-based method that can train denoising neural networks without any clean… 

Figures and Tables from this paper

Low-dose CT reconstruction by self-supervised learning in the projection domain

A self-supervised learning model that fully exploits the raw projection images to reduce noise and improve the quality of reconstructed LDCT images and reduces noise by exploiting the correlation between nearby projection images.

Wavelet subband-specific learning for low-dose computed tomography denoising

A stationary wavelet transform-assisted network employing the characteristics of high- and low-frequency domains of the wave let transform and frequency subband-specific losses defined in the wavelet domain is proposed, which acquired denoised CT images with high perceptual quality using this strategy while minimizing the objective quality loss.

Suppression of Independent and Correlated Noise with Similarity-based Unsupervised Deep Learning

The Noise2Sim theorem is proved that under mild conditions the deep denoising network trained on similar sub-images is asymptotically equivalent to that trained in the supervised learning mode, and the proposed unsupervised learning approach can be well tolerated in common tasks.

Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

A mobile deployment optimization scheme based on the supervisory mechanism model of industrial IoT, which improves the traversal of the quantum genetic algorithm by improving the genetic variation rules, thus improving the initial deployment of the network.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT that exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

References

SHOWING 1-10 OF 77 REFERENCES

Probabilistic self-learning framework for Low-dose CT Denoising

A shift-invariant property-based neural network that uses only the LDCT images to characterize both the inherent pixel correlations and the noise distribution, shaping into the probabilistic self-learning (PSL) framework to mitigate the data scarcity problem for deep learning-based LDCT denoising.

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.

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

This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity that is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.

Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging

A domain progressive 3D residual convolution network (DP-ResNet) for the LDCT imaging procedure that contains three stages: sinogram domain network (SD-net), filtered back projection (FBP), and imagedomain network (ID-net).

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

A Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising is introduced and it is demonstrated that the 3D CPCEDenoising model has a better performance, suppressing image noise and preserving subtle structures.

Modularized Data-Driven Reconstruction Framework for Non-ideal Focal Spot Effect Elimination in Computed Tomography.

A modularized data-driven CT reconstruction framework is established to mitigate the blurring effect caused by a non-ideal X-ray source with relatively large focal spot, and the proposed method enables us to obtain high-resolution images with less ideal X-Ray source.

Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction

It is shown that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

Low-Dose X-ray CT Reconstruction via Dictionary Learning

The results show that the proposed approach might produce better images with lower noise and more detailed structural features in the authors' selected cases, however, there is no proof that this is true for all kinds of structures.

Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning

A noise learning generative adversarial network coupling with least squares, structural similarity and L1 losses for low-dose CT denoising, which can effectively suppress noise and remove artifacts compared with the state-of-the-art methods.

Low-dose CT via convolutional neural network.

A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion, demonstrating a great potential of the proposed method on artifact reduction and structure preservation.
...