Prof. Deyu Meng Profile

Prof. Deyu Meng

Associate Professor at Xi'an Jiaotong Univ

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Publications (4)

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

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.

Proceedings Article | 28 May 2019 Paper

Contrast-medium anisotropy-aware tensor total variation model for robust cerebral perfusion CT reconstruction with weak radiation: a preliminary study

Yuanke Zhang, Dong Zeng, Sui Li, Yuting Liao, Zhaoying Bian, Yongbo Wang, Ji He, Xiao Jia, Yuxiang Xing, Deyu Meng, Hongbing Lu, Jianhua Ma

Proceedings Volume 11072, 110720A (2019) https://doi.org/10.1117/12.2534360

KEYWORDS: Reconstruction algorithms, Algorithm development, Computed tomography, CT reconstruction, Matrices, Optimization (mathematics), Biomedical engineering, Mathematical modeling

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Proceedings Article | 9 March 2018 Paper

CPCT-LRTDTV: cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization

Jiangjun Peng, Dong Zeng, Jianhua Ma, Yao Wang, Deyu Meng

Proceedings Volume 10573, 1057337 (2018) https://doi.org/10.1117/12.2293964

KEYWORDS: Image restoration, Computed tomography, Image quality, Hemodynamics, Reconstruction algorithms, Brain, Ischemic stroke, Cerebral blood flow, Data modeling, Imaging systems

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