Mr. Xu Dong Profile

Xu Dong

at Virginia Tech

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Proceedings Article | 1 March 2019 Paper

Sinogram interpolation for sparse-view micro-CT with deep learning neural network

Xu Dong, Swapnil Vekhande, Guohua Cao

Proceedings Volume 10948, 109482O (2019) https://doi.org/10.1117/12.2512979

KEYWORDS: Neural networks, Computed tomography, Image quality, Reconstruction algorithms, Image processing, Image restoration, Data acquisition

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In sparse-view Computed Tomography (CT), only a small number of projection images are taken around the object, and sinogram interpolation method has a significant impact on final image quality. When the amount of sparsity - the amount of missing views in sinogram data – is not high, conventional interpolation methods have yielded good results. When the amount of sparsity is high, more advanced sinogram interpolation methods are needed. Recently, several deep learning (DL) based sinogram interpolation methods have been proposed. However, those DL-based methods have mostly tested so far on computer simulated sinogram data rather experimentally acquired sinogram data. In this study, we developed a sinogram interpolation method for sparse-view micro-CT based on the combination of U-Net and residual learning. We applied the method to sinogram data obtained from sparse-view micro-CT experiments, where the sparsity reached 90%. The interpolated sinogram by the DL neural network was fed to FBP algorithm for reconstruction. The result shows that both RMSE and SSIM of CT image are greatly improved. The experimental results demonstrate that this sinogram interpolation method produce significantly better results over standard linear interpolation methods when the sinogram data are extremely sparse.

Proceedings Article | 1 March 2019 Paper

An improved physics model for multi-material identification in photon counting CT

Xu Dong, Olga Pen, Zhicheng Zhang, Guohua Cao

Proceedings Volume 10948, 109484O (2019) https://doi.org/10.1117/12.2512525

KEYWORDS: Data modeling, Sensors, X-rays, Signal attenuation, Physics, Photon counting, Chemical elements

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Photon-counting computed tomography (PCCT) with energy discrimination capabilities hold great potentials to improve the limitations of the conventional CT, including better signal-to-noise ratio (SNR), improved contrast-to-noise ratio (CNR), lower radiation dose, and most importantly, simultaneous multiple material identification. One potential way of material identification is via calculation of effective atomic number (Z_eff) and effective electron density (pe_eff) from PCCT image data. However, the current methods for calculating effective atomic number and effective electron density from PCCT image data are mostly based on semi-empirical models and accordingly are not sufficiently accurate. Here, we present a physics-based model to calculate the effective atomic number and effective electron density of various matters, including single element substances, molecular compounds, and multi-material mixtures as well. The model was validated over several materials under various combinations of energy bins. A PCCT system was simulated to generate the PCCT image data, and the proposed model was applied to the PCCT image data. Our model yielded a relative standard deviations for effective atomic numbers and effective electron densities at less than 1%. Our results further showed that five different materials can be simultaneously identified and well separated in a Z_eff − pe_eff map. The model could serve as a basis for simultaneous material identification from PCCT.

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