Dr. Jian Dong Profile

Dr. Jian Dong

at Tianjin Univ of Technology and Education

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Publications (2)

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Proceedings Article | 21 June 2024 Paper

Post-processing denoising of low-dose CT images based on GAN network

Siyuan Zhang, Jian Dong, Shuang Li

Proceedings Volume 13167, 131672K (2024) https://doi.org/10.1117/12.3029731

KEYWORDS: Image restoration, Reconstruction algorithms, Computed tomography, Image processing, Gallium nitride, Image quality, Medical image reconstruction, Super resolution, Interpolation, X-ray computed tomography

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In recent years, the damage of X-rays to the human body has received widespread attention. This paper analyzes and improves the image super-resolution reconstruction algorithm based on adversarial-generative networks that performs well in the field of image post-processing. This algorithm can largely weaken the The loss function in the convolutional network directly manages the defects of the result, thereby achieving the purpose of restoring image details more clearly. We have purposefully added structures such as filtering, feature extraction, and detail restoration to the generation network to achieve a state suitable for low-dose CT image restoration. Experimental results show that this kind of network has a more stable reconstruction effect and can obtain clearer texture and detail information.

Proceedings Article | 20 April 2021 Paper

An iterative reconstruction method for CT metal artifact reduction using L1 norm data fidelity and nonlocal TV regularization

Kazuki Chigita, Jian Dong, Hiroyuki Kudo

Proceedings Volume 11792, 117920Z (2021) https://doi.org/10.1117/12.2590726

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This paper deals with the so-called Metal Artifact Reduction (MAR) in CT. This problem aims at reconstructing a CT image with reduced metal induced artifact when the object contains metallic parts inside. We propose a new iterative reconstruction method to the MAR problem, which uses the L₁ norm for data fidelity term and Nonlocal TV regularization. In ordinary iterative reconstruction for CT, the least-squares error || A ^→x - ^→b|| ²₂ Is used as data fidelity term for image reconstruction. However, it is well-known that the least-squares criterion is sensitive to the existence of abnormal (inconsistent) data in the measurement ^→b, such as projection data passing through the metallic parts in this work. A simple reasonable method to identify the location of metallic parts in the sinogram and exclude the corresponding projection data from the data fitting is to use the L₁norm error || A ^→x - ^→b|| ¹₁ . Furthermore, the power of proposed method to reduce the metal artifact can be significantly improved by adding Nonlocal Total Variation (NLTV) regularization term into the cost function. Compared to existing approaches to the MAR problem, the proposed method possesses the following attractive feature. Almost every approach to MAR consists of two-step computations. The first step detects the metallic parts in the sinogram and the second step performs image reconstruction after interpolating or excluding the projection data corresponding to the identified metallic parts. On the other hand, the proposed method consists of only a single computational step, i.e. single iterative minimization of a convex cost function, leading to smartly unifying the two steps into a single step.

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