X-ray image super-resolution reconstruction based on a multiple distillation feedback network

Du, Yan-Bin; Jia, Rui-Sheng; Cui, Zhe; Yu, Juan; Sun, Hong-Mei; Zheng, Yongguo

doi:10.1007/s10489-020-02123-2

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…Moreover, NSS models capture the statistical consistencies of X-ray images effectively. This means that the NIQE is a suitable image quality metric to be used in X-ray imaging [77][78][79][80] , and specifically, DF imaging. The NIQE score is calculated in regions of high image contrast, and hence, the NIQE model may mistake artefacts for signal.…”

Section: Weakly-attenuating Sample: Wattle Flower (Wattle)mentioning

confidence: 99%

Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Alloo

Morgan

Paganin

et al. 2023

Sci Rep

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Speckle-based phase-contrast X-ray imaging (SB-PCXI) can reconstruct high-resolution images of weakly-attenuating materials that would otherwise be indistinguishable in conventional attenuation-based X-ray imaging. The experimental setup of SB-PCXI requires only a sufficiently coherent X-ray source and spatially random mask, positioned between the source and detector. The technique can extract sample information at length scales smaller than the imaging system’s spatial resolution; this enables multimodal signal reconstruction. “Multimodal Intrinsic Speckle-Tracking” (MIST) is a rapid and deterministic formalism derived from the paraxial-optics form of the Fokker–Planck equation. MIST simultaneously extracts attenuation, refraction, and small-angle scattering (diffusive dark-field) signals from a sample and is more computationally efficient compared to alternative speckle-tracking approaches. Hitherto, variants of MIST have assumed the diffusive dark-field signal to be spatially slowly varying. Although successful, these approaches have been unable to well-describe unresolved sample microstructure whose statistical form is not spatially slowly varying. Here, we extend the MIST formalism such that this restriction is removed, in terms of a sample’s rotationally-isotropic diffusive dark-field signal. We reconstruct multimodal signals of two samples, each with distinct X-ray attenuation and scattering properties. The reconstructed diffusive dark-field signals have superior image quality—as measured by the naturalness image quality evaluator, signal-to-noise ratio, and azimuthally averaged power-spectrum—compared to our previous approaches which assume the diffusive dark-field to be a slowly varying function of transverse position. Our generalisation may assist increased adoption of SB-PCXI in applications such as engineering and biomedical disciplines, forestry, and palaeontology, and is anticipated to aid the development of speckle-based diffusive dark-field tensor tomography.

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Section: Weakly-attenuating Sample: Wattle Flower (Wattle)mentioning

confidence: 99%

Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Alloo

Morgan

Paganin

et al. 2023

Sci Rep

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“…In recent applications of deep learning to X-ray imagery for security screening and medical diagnostics, researchers have utilised advanced techniques like denoising and super-resolution to enhance image quality [4,5]. These techniques enable the extraction of more detailed information from X-ray images, leading to better diagnostic accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques

Yagoub,

Kasem,

Kang

2024

Applied Sciences

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This study addresses the field of X-ray security screening and focuses on synthesising realistic X-ray images using advanced generative models. Insufficient training data in this area pose a major challenge, which we address through innovative data augmentation techniques. We utilise the power of generative adversarial networks (GANs) and conditional GANs (cGANs), in particular the Pix2Pix and Pix2PixHD models, to investigate the generation of X-ray images from various inputs such as masks and edges. Our experiments conducted on a Korean dataset containing dangerous objects relevant to security screening show the effectiveness of these models in improving the quality and realism of image synthesis. Quantitative evaluations based on metrics such as PSNR, SSIM, LPIPS, FID, and FSIM, with scores of 19.93, 0.71, 0.12, 29.36, and 0.54, respectively, show the superiority of our strategy, especially when integrated with hybrid inputs containing both edges and masks. Overall, our results highlight the potential of advanced generative models to overcome the challenges of data scarcity in X-ray security screening and pave the way for more efficient and accurate inspection systems.

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“…The learningbased method uses the convolutional neural network (CNN) to establish a nonlinear mapping relationship between LR and HR images. In the past 2 years, superresolution reconstruction methods based on deep learning models have also been gradually applied to the task of HR CXR image acquisition (44)(45)(46). The models include deep recursive neural network (DRCN) (47), HR Network (HRNet) (48), and super-resolution generative adversarial network (SRGAN) (49).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification

Sun¹,

Ren²,

Teng³

et al. 2023

Quant Imaging Med Surg

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Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models.Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions:The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

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X-ray image super-resolution reconstruction based on a multiple distillation feedback network

Cited by 6 publications

References 39 publications

Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification

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X-ray image super-resolution reconstruction based on a multiple distillation feedback network

Cited by 6 publications

References 39 publications

M﻿ultimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

M﻿ultimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification

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Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field