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Author

Guang Li

Other affiliations: Southeast University
Bio: Guang Li is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Imaging phantom & Image restoration. The author has an hindex of 4, co-authored 8 publications receiving 377 citations. Previous affiliations of Guang Li include Southeast University.

Papers
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Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.
Abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

257 citations

Journal ArticleDOI
TL;DR: In this article, a semi-supervised deep learning approach was proposed to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Abstract: Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel $1\times1$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

242 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the low-dose CT image quality, which incorporates3-D volumetric information to improved the image quality.
Abstract: Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.

195 citations

Journal ArticleDOI
TL;DR: The proposed novel calibration method demonstrates higher calibration accuracy and more robustness than the benchmark algorithm, and can obtain accurate geometric parameters of a CBCT system with a circular trajectory.
Abstract: Purpose Cone-beam (CB) CT is a powerful noninvasive imaging modality, and is widely used in many applications. Accurate geometric parameters are essential for high-quality image reconstruction. Usually, a CBCT system with higher spatial resolution, particularly on the order of microns or nanometers, will be more sensitive to the parametric accuracy. Here, we propose a novel calibration method combining a simple phantom containing ball bearing markers and an advanced optimization procedure. This method can be applied to CBCT with reproducible geometry and frame-to-frame invariant geometric parameters. Methods Our proposed simplex-simulated annealing procedure minimizes the cost function that associates the geometrical parameters with the degree to which the back projections of the ball bearings in projections from various viewing angles converge, and the global minimum of the cost function corresponds to the actual geometric parameters. Specifically, six geometric parameters can be directly obtained by minimizing the cost function, and the last parameter, the distance from source to rotation axis (SRD), can be obtained using prior knowledge of the phantom - the spacing between the two ball bearings. Results Numerical simulation was performed to validate that the proposed method with various noise levels. With the proposed method, the mean errors and standard deviations can be reduced to ∼10% and less than 1/3 of a competing benchmark method in the case of strong Gaussian noise (sigma = 200% of the pixel size) and large tilt angle (tilt angle = - 4 ∘ ). The calibration experiments with micro-CT and high-resolution CT scanners demonstrate that the proposed method recovers imaging parameters accurately, leading to superior image quality. Conclusion The proposed method can obtain accurate geometric parameters of a CBCT system with a circular trajectory. While in the case of micro-CT the proposed method has a performance comparable to the competing method, for high-resolution CT, which is more sensitive to geometric calibration, the proposed method demonstrates higher calibration accuracy and more robustness than the benchmark algorithm.

21 citations

Journal ArticleDOI
21 May 2018
TL;DR: A new “collimation” design dedicated to capture a small-angle scattering radiographic image directly, which carries critical pathological information for differentiation between normal and abnormal tissues is proposed.
Abstract: X-ray imaging techniques, including X-ray radiography and computed tomography, have been in use for decades and proven effective and indispensable in diagnosis and therapy due to their fine resolution and fast acquisition speed. However, the innate disadvantage of X-ray is the poor soft tissue contrast. Small-angle scattering signals were shown to provide unique information about the abnormality of soft tissues that is complementary to the traditional attenuation image. Currently, there is no effective small-angle scattering detection system. In this paper, we propose a new “collimation” design dedicated to capture a small-angle scattering radiographic image directly, which carries critical pathological information for differentiation between normal and abnormal tissues. Our design consists of two interlaced gratings so that both primary flux and Compton scattering photons are effectively blocked to leave the apertures mainly open to small-angle scattering photons. Theoretical analysis and Monte Carlo simulations demonstrate that small-angle scattering radiography is feasible with our proposed technology.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

1,053 citations

Journal ArticleDOI
TL;DR: This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
Abstract: Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.

473 citations

Journal ArticleDOI
TL;DR: In this article, a modularized neural network for low-dose CT (LDCT) was proposed and compared with commercial iterative reconstruction methods from three leading CT vendors, and the learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion.
Abstract: Commercial iterative reconstruction techniques help to reduce CT radiation dose but altered image appearance and artifacts limit their adoptability and potential use. Deep learning has been investigated for low-dose CT (LDCT). Here we design a modularized neural network for LDCT and compared it with commercial iterative reconstruction methods from three leading CT vendors. While popular networks are trained for an end-to-end mapping, our network performs an end-to-process mapping so that intermediate denoised images are obtained with associated noise reduction directions towards a final denoised image. The learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion. Our network was trained with the Mayo LDCT Dataset, and tested on separate chest and abdominal CT exams from Massachusetts General Hospital. The best deep learning reconstructions were systematically compared to the best iterative reconstructions in a double-blinded reader study. This study confirms that our deep learning approach performed either favorably or comparably in terms of noise suppression and structural fidelity, and is much faster than the commercial iterative reconstruction algorithms.

265 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.
Abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

257 citations

Journal ArticleDOI
TL;DR: In this article, a semi-supervised deep learning approach was proposed to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Abstract: Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel $1\times1$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

242 citations