Dr. Chuang Niu Profile

Dr. Chuang Niu

at Rensselaer Polytechnic Institute

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Publications (5)

Proceedings Article | 10 November 2022 Presentation + Paper

Multi-view x-ray dissectography improves nodule detection

Chuang Niu, Giridhar Dasegowda, Pingkun Yan, Mannudeep Kalra, Ge Wang

Proceedings Volume 12242, 1224217 (2022) https://doi.org/10.1117/12.2637782

KEYWORDS: Lung, X-rays, Radiography, X-ray imaging, Sensors, 3D image processing, X-ray computed tomography, Detection and tracking algorithms, 3D modeling, Lung cancer

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Proceedings Article | 18 October 2022 Open Access

X-ray dissectography enables stereotography

Chuang Niu, Ge Wang

Proceedings Volume 12304, 123041T (2022) https://doi.org/10.1117/12.2647278

KEYWORDS: X-rays, Radiography, X-ray imaging, 3D acquisition, Lung, Imaging systems, Image segmentation, X-ray computed tomography, 3D image processing, Tomography

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Proceedings Article | 14 October 2022 Presentation + Paper

Self-supervised dual-domain network for low-dose CT denoising

Chuang Niu, Mengzhou Li, Xiaodong Guo, Ge Wang

Proceedings Volume 12242, 122420H (2022) https://doi.org/10.1117/12.2633197

KEYWORDS: Denoising, Image processing, X-ray computed tomography, Reconstruction algorithms, Signal attenuation, Data modeling, Computed tomography, Image quality, Radiation effects, Machine learning

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Proceedings Article | 4 April 2022 Poster + Paper

Effects of image denoising on quantitative material decomposition in photon-counting spectral computed tomography

Connor Evans, Mengzhou Li, Chuang Niu, Ge Wang, Ryan Roeder

Proceedings Volume 12031, 120312Y (2022) https://doi.org/10.1117/12.2611828

KEYWORDS: Gadolinium, Image denoising, Calcium, Iodine, Spatial resolution, Tissues, Image quality, Image classification, Spectral computed tomography, X-ray computed tomography, X-ray medical imaging

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Material decomposition algorithms enable discrimination and quantification of multiple contrast agent and tissue compositions in spectral image datasets acquired by photon-counting computed tomography (PCCT). Image denoising has been shown to improve PCCT image reconstruction quality and feature recognition while preserving fine image detail. Reduction of image artifacts and noise could also improve the accuracy of material decomposition but the effects of denoising on material decomposition have not been investigated. In particular, deep learning methods can reduce inherent PCCT image noise without using a system-based or assumed prior noise model. Therefore, the objective of this study was to investigate the effects of image denoising on quantitative material decomposition in the absence of an influence of spatial resolution on feature recognition. Phantoms comprising multiple pure and spatially uniform contrast agent (gadolinium, iodine) and tissue (calcium, water) compositions were imaged by PCCT with four energy thresholds chosen to normalize photon counts and leverage contrast agent k-edges. Image denoising was performed by the established blockmatching and 3D-filtering (BM3D) algorithm or deep learning using convolutional neural networks. Material decomposition was performed on as-acquired, BM3D-denoised, and deep-learning-denoised datasets using constrained maximum likelihood estimation and compared to known material concentrations in the phantom. Image denoising by BM3D and deep learning improved the quantitative accuracy of material concentrations determined by material decomposition compared to ground truth, as measured by the root-mean-squared error. Material classification was not improved by image denoising compared with as-acquired images, suggesting that material decomposition was robust against inherent acquisition noise when feature recognition was not challenged by the system spatial resolution. Deeplearning-denoised images balanced preservation of local detail compared to more aggressive smoothing with BM3D, as measured by line profiles across features.

Proceedings Article | 9 September 2021 Presentation + Paper

Multiple window learning for CT metal artifact reduction

Chuang Niu, Mengzhou Li, Ge Wang

Proceedings Volume 11840, 1184015 (2021) https://doi.org/10.1117/12.2596239

KEYWORDS: Metals, Computed tomography, X-ray computed tomography, Spine, Computer simulations, Visualization, Tissues, Inspection, Image processing, Germanium

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