A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography With Incomplete Data

Fu, Jian; Dong, Jianbing; Zhao, Feng

doi:10.1109/tip.2019.2947790

Cited by 31 publications

(12 citation statements)

References 47 publications

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“…k = 2, .., M − 1, λ min and λ max are defined in equation (26), ∇λ = π/180 and λ 0 is the start angle of the scanning helix. For p = 7π, λ min = −2.1720, λ max = 7.9625, and for p = 14π, λ min = −2.1875, λ max = 8.1025.…”

Section: Data Preparation 1) Ct Image Labelsmentioning

confidence: 99%

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction

Wang¹,

Xia²,

He³

et al. 2022

Preprint

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Section: Data Preparation 1) Ct Image Labelsmentioning

confidence: 99%

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction

Wang¹,

Xia²,

He³

et al. 2022

Preprint

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“…The second approach is sinogram domain data inpainting, which preprocesses a neural network in a few-view sinogram domain and synthesizes it into a complete view sinogram [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76]. Applying analytical image reconstruction algorithms such as filtered back projection (FBP) directly to sparse view data will result in poor image quality and serious fringe artifacts.…”

Section: Applications In Different Domainsmentioning

confidence: 99%

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang

Yang

Shi

2022

Complex Intell. Syst.

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Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

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“…However, these information may contain a great quantity of redundancy, noise, or even missing feature values [6][7][8]. Nowadays, how to deal with missing values, reduce redundant features, and simplify the complexity of the clas-sification model, so as to improve the generalization ability of model classification is a huge challenge we are facing [9][10][11][12][13][14][15]. As an important step of data preprocessing, feature selection based on granular computing has been widely used in knowledge discovery, data mining, machine learning, and other fields [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

Yuan

et al. 2022

Complex Intell. Syst.

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For incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimistic neighborhood multi-granulation tolerance joint entropy (PTSIJE), which can be used to incomplete neighborhood decision systems. First, from the algebra view, four kinds of neighborhood multi-granulation self-information measures of decision variables are proposed by using the upper and lower approximations of NMRS. We discuss the related properties, and find the fourth measure-lenient neighborhood multi-granulation self-information measure (NMSI) has better classification performance. Then, inspired by the algebra and information views simultaneously, a feature selection method based on PTSIJE is proposed. Finally, the Fisher score method is used to delete uncorrelated features to reduce the computational complexity for high-dimensional gene datasets, and a heuristic feature selection algorithm is raised to improve classification performance for mixed and incomplete datasets. Experimental results on 11 datasets show that our method selects fewer features and has higher classification accuracy than related methods.

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A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography With Incomplete Data

Cited by 31 publications

References 47 publications

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

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