Mr. Qi Gao Profile

Qi Gao

at Fudan Univ

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Author

Publications (9)

Proceedings Article | 14 October 2022 Presentation + Paper

CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising

Qi Gao, Hongming Shan

Proceedings Volume 12242, 122420I (2022) https://doi.org/10.1117/12.2634939

KEYWORDS: Diffusion, Computed tomography, Denoising, Performance modeling, Image processing, Image denoising, Visual process modeling, Data modeling, Image quality, Process modeling

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Proceedings Article | 16 March 2020 Paper

Leveraging deep generative model for direct energy-resolving CT imaging via existing energy-integrating CT images

Lisha Yao, Sui Li, Danyang Li, Manman Zhu, Qi Gao, Shanli Zhang, Zhaoying Bian, Jing Huang, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, 113124U (2020) https://doi.org/10.1117/12.2548992

KEYWORDS: Computed tomography, Convolution, Image classification, Data modeling, Image analysis, Lithium, Photon counting, Sensors, Spatial resolution, Neural networks

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Proceedings Article | 16 March 2020 Paper

Non-local texture learning approach for CT imaging problems using convolutional neural network

Sui Li, Lisha Yao, Manman Zhu, Danyang Li, Qi Gao, Xinyu Zhang, Rikui Zhong, Zhaoying Bian, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, 113124B (2020) https://doi.org/10.1117/12.2548949

KEYWORDS: Computed tomography, Convolutional neural networks, Convolution, 3D image processing, CT reconstruction, Reconstruction algorithms, 3D acquisition, Neural networks, Lithium, Evolutionary algorithms

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Proceedings Article | 16 March 2020 Paper

Unsupervised data fidelity enhancement network for spectral CT reconstruction

Danyang Li, Sui Li, Manman Zhu, Qi Gao, Zhaoying Bian, Haiyun Huang, Shanli Zhang, Jing Huang, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, 113124D (2020) https://doi.org/10.1117/12.2548893

KEYWORDS: Computed tomography, X-ray computed tomography, CT reconstruction, Data modeling, Dual energy imaging, Spectral models, Machine learning, Signal attenuation, Numerical simulations, Imaging systems

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Proceedings Article | 16 March 2020 Presentation + Paper

Semi-supervised learned sinogram restoration network for low-dose CT image reconstruction

Mingqiang Meng, Sui Li, Lisha Yao, Danyang Li, Manman Zhu, Qi Gao, Qi Xie, Qian Zhao, Zhaoying Bian, Jing Huang, Deyu Meng, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, 113120B (2020) https://doi.org/10.1117/12.2548985

KEYWORDS: Machine learning, Reconstruction algorithms, X-ray computed tomography, Computed tomography, CT reconstruction, Image processing, Image filtering, Denoising, Data modeling, Lithium

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With the development of deep learning (DL), many deep learning (DL) based algorithms have been widely used in the low-dose CT imaging and achieved promising reconstruction performance. However, most DL-based algorithms need to pre-collect a large set of image pairs (low-dose/high-dose image pairs) and trains networks in a supervised end-to-end manner. Actually, it is not feasible in clinical to obtain such a large amount of paired training data, especially for high-dose ones. Therefore, in this work, we present a semi-supervised learned sinogram restoration network (SLSR-Net) for low-dose CT image reconstruction. The presented SLSR-Net consists of supervised sub-network and unsupervised sub-network. Specifically, different from the traditional supervised DL networks which only use low-dose/high-dose sinogram pairs, the presented SLSR-Net method is capable of feeding only a few supervised sinogram pairs and massive unsupervised low-dose sinograms into the network training procedure. The supervised pairs are used to capture critical features (i.e., noise distribution, and tissue characteristics) latent in a supervised way and the unsupervised sub-network efficiently learns these features using a conventional weighted least-squares model with a regularization term. Moreover, another contribution of the presented SLSR-Net method is to adaptively transfer learned feature distribution from supervised subnetwork with the paired sinograms to unsupervised sub-network with unlabeled low-dose sinograms to obtain high-fidelity sinogram with a Kullback-Leibler divergence. Finally, the filtered backprojection algorithm is used to reconstruct CT images from the obtained sinograms. Real patient datasets are used to evaluate the performance of the presented SLSR-Net method and the corresponding experimental results show that compared with the traditional supervised learning method, the presented SLSR-Net method achieves competitive performance in terms of noise reduction and structure preservation in low-dose CT imaging.

Proceedings Article | 16 March 2020 Paper

Combined global and local information for blind CT image quality assessment via deep learning

Qi Gao, Sui Li, Manman Zhu, Danyang Li, Zhaoying Bian, Qingwen Lv, Dong Zeng, Jianhua Ma

Proceedings Volume 11316, 1131615 (2020) https://doi.org/10.1117/12.2548953

KEYWORDS: Image quality, Computed tomography, X-ray computed tomography, Image fusion, Distortion, Lithium, Convolutional neural networks, Image analysis, Quality measurement, Computer simulations

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Proceedings Article | 16 March 2020 Paper

Deep neural networks for low-dose CT image reconstruction via cooperative meta-learning strategy

Manman Zhu, Sui Li, Danyang Li, Qi Gao, Shanli Zhang, Haiyun Huang, Zhaoying Bian, Jing Huang, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, 1131243 (2020) https://doi.org/10.1117/12.2548950

KEYWORDS: Computed tomography, Data modeling, X-ray computed tomography, CT reconstruction, Neural networks, Image quality, Visualization, Image processing, Lawrencium, Statistical modeling

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Proceedings Article | 16 March 2020 Paper

Progressive transfer learning strategy for low-dose CT image reconstruction with limited annotated data

Mingqiang Meng, Danyang Li, Sui Li, Manman Zhu, Lei Wang, Qi Gao, Zhaoying Bian, Xinyu Zhang, Jing Huang, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, 1131242 (2020) https://doi.org/10.1117/12.2548946

KEYWORDS: Computed tomography, Convolution, CT reconstruction, Image restoration, Image quality, Medical imaging, Network architectures, Lithium, Cancer, Diagnostics

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Proceedings Article | 16 March 2020 Paper

Semi-supervised noise distribution learning for low-dose CT restoration

Lei Wang, Qi Gao, Mingqiang Meng, Sui Li, Manman Zhu, Danyang Li, Gaofeng Chen, Dong Zeng, Qi Xie, Qian Zhao, Zhaoying Bian, Deyu Meng, Jianhua Ma

Proceedings Volume 11312, 1131244 (2020) https://doi.org/10.1117/12.2548944

KEYWORDS: Computed tomography, X-ray computed tomography, Image restoration, Machine learning, Lithium, Medical imaging, Gallium nitride, Data modeling, Biomedical engineering, Image processing

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Fully supervised deep learning (DL) methods have been widely used in low-dose CT (LDCT) imaging field and can usually achieve high accuracy results. These methods require a large labeled training set which consists of pairs of LDCT images as well as their corresponding high-dose CT (HDCT) ones. They successfully learn intermediate concept of features describing important components in CT images, such as noise distribution, and structure details, which is important to capture dependencies from LDCT image to HDCT ones. However, it should be noted that it is quite time-consuming and costly to obtain such a large of labeled CT images especially the HDCT images are limited in clinics. In comparison, lots of unlabeled LDCT images are usually easily accessible and massive critical information latent in the unlabeled LDCT can be leveraged to further boost restoration performance. Therefore, in this work, we present a semi-supervised noise distribution learning network to suppress noise-induced artifacts in the LDCT images. For simplicity, the presented network in termed as "SNDL-Net". The presented SNDL-Net consists of two sub-networks, i.e., supervised network, and unsupervised network. In the supervised network, the LDCT/HDCT image pairs are used for network training. And the unsupervised network considers the complex noise distribution in the LDCT images, and model the noise with a Gaussian mixture framework, then learns the proper gradient of LDCT images in a purely unsupervised manner. Similar with the supervised network training, the gradient information in a large of unlabeled LDCT images can be used for unsupervised network training. Moreover, to learn the noise distribution accurately, the discrepancy between the learned noise distribution in the supervised network and learned noise distribution in the unsupervised network can be modeled by a Kullback-Leibler (KL) divergence. Experiments on the Mayo clinic dataset verify the method is effective in low-dose CT image restoration with only a small amount of labeled data compared to previous supervised deep learning methods.

Showing 5 of 9 publications

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