Open AccessPosted Content
Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction
TLDR
Deep learning networks can effectively reconstruct rich high frequency structural information without streaking artefact commonly seen in sparse view CT reconstruction with projectiondomain network, image domain network, and comprehensive network combining projection and image domains.Abstract:
X-ray Computed Tomography (CT) imaging has been widely used in clinical diagnosis, non-destructive examination, and public safety inspection. Sparse-view (sparse view) CT has great potential in radiation dose reduction and scan acceleration. However, sparse view CT data is insufficient and traditional reconstruction results in severe streaking artifacts. In this work, based on deep learning, we compared image reconstruction performance for sparse view CT reconstruction with projection domain network, image domain network, and comprehensive network combining projection and image domains. Our study is executed with numerical simulated projection of CT images from real scans. Results demonstrated deep learning networks can effectively reconstruct rich high frequency structural information without streaking artefact commonly seen in sparse view CT. A comprehensive network combining deep learning in both projection domain and image domain can get best results.read more
Citations
More filters
Journal ArticleDOI
High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains
TL;DR: This work proposed a method of sparsely sampled CT reconstruction from a new perspective - unlike iterative reconstruction, and developed an optimal deep learning-based sparse sampling reconstruction technique by evaluating image quality with deep learning technologies.
Journal ArticleDOI
Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.
TL;DR: The sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction, and the U-Net generator and patch-design discriminator in SI-GAN are proposed to make the network suitable for standard medical CT images.
Journal ArticleDOI
A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT.
TL;DR: This work focuses on a new prototype of helical CT which equipped with sparsely spaced multidetector and multi-slit collimator in the axis direction, and proposes a deep learning-based function optimization method for this ill-posed inverse problem.
Journal ArticleDOI
Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains
Wei Fang,Liang Li,Zhiqiang Chen +2 more
TL;DR: Quantitative analysis shows that deep learning based methods are promising in solving the problem of non-uniformity correction for photon-counting detectors in image domain, projection domain and the polar coordinate system.
Journal ArticleDOI
Learning to Reconstruct CT Images From the VVBP-Tensor
TL;DR: Zhang et al. as discussed by the authors proposed a view-by-view backprojection tensor (VVBP-Tensor) to preserve fine details of the CT image.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
Book
Principles of Computerized Tomographic Imaging
TL;DR: Properties of Computerized Tomographic Imaging provides a tutorial overview of topics in tomographic imaging covering mathematical principles and theory and how to apply the theory to problems in medical imaging and other fields.