Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT

Umehara, Kensuke; Ota, Junko; Ishida, Takayuki

doi:10.1007/s10278-017-0033-z

Cited by 156 publications

(67 citation statements)

References 22 publications

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“…A deep cascade of CNNs has been shown to preserve anatomical structure up to 11‐fold undersampling using cardiac MRI . In another study using CT, a single image superresolution approach based on CNN was applied in a publicly available chest CT image dataset to generate high‐resolution CT images, which are preferred for interstitial lung disease detection. This method outperformed the traditional compressed sensing‐based approaches used in MR image reconstruction.…”

Section: Deep Learning In Radiology: State Of the Artmentioning

confidence: 99%

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Mazurowski

Buda

Saha

et al. 2018

Magnetic Resonance Imaging

345

208

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Section: Deep Learning In Radiology: State Of the Artmentioning

confidence: 99%

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Mazurowski

Buda

Saha

et al. 2018

Magnetic Resonance Imaging

345

208

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“…Later on, these networks started getting used for super-resolution (SR) imaging [93] and some of the first applications focused on photographs (see, e.g., [93][94][95][96]) and movies [97]. They were rapidly used for the resolution enhancement of, for example, satellite images [98,99] and medical images [100], like magnetic resonance imaging [101,102] and CT [103][104][105][106][107]. The success of a lot of these SR applications is explained by the emergence of generative adversarial networks (GANs) [108] which are commonly known for their strength at generating realistic "fake" images [109].…”

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confidence: 99%

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

2020

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Fluid flow characteristics are important to assess reservoir performance. Unfortunately, laboratory techniques are inadequate to know these characteristics, which is why numerical methods were developed. Such methods often use computed tomography (CT) scans as input but this technique is plagued by a resolution versus sample size trade-off. Therefore, a super-resolution method using generative adversarial neural networks (GANs) was used to artificially improve the resolution. Firstly, the influence of resolution on pore network properties and single-phase, unsaturated, and two-phase flow was analysed to verify that pores and pore throats become larger on average and surface area decreases with worsening resolution. These observations are reflected in increasingly overestimated single-phase permeability, less moisture uptake at lower capillary pressures, and high residual oil fraction after waterflooding. Therefore, the super-resolution GANs were developed which take low (12 µm) resolution input and increase the resolution to 4 µm, which is compared to the expected high-resolution output. These results better predicted pore network properties and fluid flow properties despite the overestimation of porosity. Relevant small pores and pore surfaces are better resolved thus providing better estimates of unsaturated and two-phase flow which can be heavily influenced by flow along pore boundaries and through smaller pores. This study presents the second case in which GANs were applied to a super-resolution problem on geological materials, but it is the first one to apply it directly on raw CT images and to determine the actual impact of a super-resolution method on fluid predictions.Materials 2020, 13, 1397 2 of 33 examples requires detailed knowledge on fluid storage and flow properties which could be estimated through experiments, either carried out in specialized laboratories or deduced from digital rocks. Laboratory measurements, however, require expensive and complex set-ups, take a long time to complete, are often destructive, do not always entirely capture the studied properties and on top of that, experiments often fail to produce reproducible results between laboratories [9,[12][13][14][15][16][17][18][19]. Therefore, numerical solutions have been developed allowing to compute fluid storage and flow properties on digital rocks [7,[20][21][22][23]. Such digital rocks are acquired through direct imaging or numerical simulations of rocks.Computed tomography (CT) is a technique to rapidly capture the three-dimensional structure of materials [22,24,25]. However, like any imaging technique, CT is plagued by a sample size versus resolution trade-off, which is adversely affecting fluid flow simulations. Both a high resolution and sufficiently large volume are required for accurate fluid flow estimations at the pore-scale [9,[26][27][28]. Resolution impacts how well pore network topology and pore surfaces are resolved. At lower resolution, i.e., lower resolving power, pore sizes are often overestimated and specific surf...

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“…The object of this study is that minimized loss function ( ) Lv. (6) where n is the number of images in a mini-batch. The weight factor of each layer was updated using the error back-propagation with adaptive moment estimation optimizer 8 , which is a stochastic optimization technique.…”

Section: Methodsmentioning

confidence: 99%

Projection super-resolution based on convolutional neural network for computed tomography

Tang

Zhang

et al. 2019

15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine

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The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projectiondomain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of low-and high-resolution projection. The network label is high-resolution projection and the input is its corresponding interpolation data after down sampling. FDK algorithm is utilized for three-dimensional image reconstruction and one slice of reconstruction image is taken as an example to evaluate the performance of the proposed method. Qualitative and quantitative results show that the proposed method is potential to improve the resolution of projection and enables the reconstructed image with higher quality.

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Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT

Cited by 156 publications

References 22 publications

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

Projection super-resolution based on convolutional neural network for computed tomography

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