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Shanzhou Niu

Researcher at Southern Medical University

Publications -  33
Citations -  926

Shanzhou Niu is an academic researcher from Southern Medical University. The author has contributed to research in topics: Iterative reconstruction & Imaging phantom. The author has an hindex of 12, co-authored 31 publications receiving 698 citations. Previous affiliations of Shanzhou Niu include University of Texas Southwestern Medical Center & University of Texas at Dallas.

Papers
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Sparse-view x-ray CT reconstruction via total generalized variation regularization.

TL;DR: Experimental results show that the present PWLS-TGV method can achieve images with several noticeable gains over the original TV-based method in terms of accuracy and resolution properties.
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A Simple Low-Dose X-Ray CT Simulation From High-Dose Scan

TL;DR: Experimental results demonstrated that the present low-dose CT simulation strategy can generate accurate low- dose CT sinogram data from high-dose scans in terms of qualitative and quantitative measurements.
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Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter

TL;DR: The proposed average image-induced nonlocal means (aviNLM) filter has useful potential for radiation dose reduction by lowering the mAs in SCT imaging, and it may be useful for some other clinical applications, such as in myocardial perfusion imaging and radiotherapy.
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Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

TL;DR: Wang et al. as mentioned in this paper explored the potential of convolutional sparse coding (CSC) in sparse-view computed tomography (CT) reconstruction, without the necessity of dividing the image into overlapped patches in DL-based methods.
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Iterative Image Reconstruction for Sparse-View CT Using Normal-Dose Image Induced Total Variation Prior

TL;DR: Experimental results show that the present PWLS-ndiTV approach for sparse-view CT image reconstruction can achieve noticeable gains over the existing similar approaches in terms of noise reduction, resolution-noise tradeoff, and low-contrast object detection.