• Corpus ID: 244798687

Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism

  title={Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism},
  author={Minghan Fu and Yanhua Duan and Zhaoping Cheng and Wenjian Qin and Ying Wang and Dong Liang and Zhanli Hu},
Reducing the radiation exposure for patients in Total-body CT scan has attracted extensive attention in the medical imaging community. Given the fact that low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning based research work has introduced various network architectures. However, most of these methods only adopt Normal-dose CT (NDCT) images as ground… 



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.

MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction

This paper proposes a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces, and demonstrates superior performance for semi-supervised learning.

Low-dose CT denoising with convolutional neural network

  • Hu ChenYi Zhang Ge Wang
  • Physics, Medicine
    2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
  • 2017
Visual and quantitative evaluation demonstrates a competing performance of the proposed noise reduction method via deep neural network without accessing original projection data for low-dose CT via deep convolutional neural network.

Optimizing non-local means for denoising low dose CT

The potential to increase the peak signal-to-noise ratio (PSNR) by over 4 dB when denoising low dose phantom CT images was used and the filter's sensitivity to adjustment of each of its parameters was quantitatively demonstrated.

LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT

This paper unfolds the state-of-the-art “fields of experts”-based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a learned experts’ assessment-based reconstruction network (LEARN) for sparse-data CT, and demonstrates the feasibility and merits of the LEARN network.

CT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels

The experiments show that the proposed parameter-dependent framework can obtain competitive performance compared to the original network trained with either specific or mixed geometry and dose level, which can efficiently save extra training costs for multiple geometries and dose levels.

Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods.

Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT.

The results demonstrate that bilateral filtering incorporating a CT noise model can achieve a significantly better noise-resolution trade-off than a series of commercial reconstruction kernels and can be translated into substantial dose reduction.

CBCT-based Synthetic CT Generation using Deep-attention CycleGAN for Pancreatic Adaptive Radiotherapy.

A deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy and validated the dose calculation accuracy carried by sCT.

Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain.

The authors propose a projection domain multiscale penalized weighted least squares (PWLS) method, in which the angular sampling rate is explicitly taken into consideration to account for the possible variation of interview sampling rate in advanced clinical or preclinical applications.