Dr. Jingwen Feng Profile

Dr. Jingwen Feng

at Tianjin Univ

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Publications (2)

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Proceedings Article | 12 March 2020 Paper

A flow cell imaging system combined diffraction and bright field microscopy

Siqi Yuan, Yu Sa, Pengpeng Sun, Yuangguang Han, Jingwen Feng

Proceedings Volume 11434, 1143405 (2020) https://doi.org/10.1117/12.2540568

KEYWORDS: Diffraction, Lutetium, Microscopy, Biomedical optics, Imaging systems, Flow cytometry, Microscopes, Yeast, Optical microscopy, Biological research

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Diffraction imaging flow cytometry is a new biological cell research method developed recently, which can get abundant information of 3D morphology inside the cell without staining. However, the pattern of diffraction image is non-intuitive, and cannot be directly classified by the observer. On the contrary, the bright field images obtained by microscope are clear enough to be directly perceived through the senses by researchers. This paper will introduce a new flow cell imaging design incorporating the merits of these two kinds of methods that can obtain diffraction image and bright field image of the same cell simultaneously, which is based on the infinite microscopic architecture with two optical paths. The first path gets the diffraction image by defocusing the shared object lens, meanwhile, the second path gets the bright-field microscopic image by adjustable lens compensating for the defocus. In the new system diffraction and microscopic images of yeasts were captured with illumination of 532nm laser and 450nm LED respectively. Two classification models were set up for recognition of yeast-budding-state with diffraction images and microscopic images independently by using GLCM (Grayscale Co-occurrence Matrix) feature extraction method, which got the highest 97% accuracy of classification with diffraction images compared to the 94% with microscopic images.

Proceedings Article | 12 March 2020 Paper

A method for metal artifacts reduction using virtual dual-energy CT images generated from single energy CT scans

Sheng-Zhuang Miao, Jing-Wen Feng, Cheng-Wen Yang, Yu Sa

Proceedings Volume 11434, 114340O (2020) https://doi.org/10.1117/12.2543456

KEYWORDS: Computed tomography, Metals, Image processing, Signal attenuation, CT reconstruction, Standards development, Radiotherapy, Data processing, Image analysis, Scanners

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A new method to remove metal artifacts utilizing virtual dual-energy CT image sets generated from monoenergetic CT images and dual-energy CT subtraction is presented in this work. CT images were derived from Optima CT580 (General Electric Company, Fairfield, Connecticut, USA). Optimized conversion model from CT numbers to linear attenuation coefficients (LAC) was applied to calculate an accurate LAC map at specific energy. According to mass attenuation coefficients (MAC) of base materials from the National Institute of Standards and Technology (NIST), a LAC map at another higher energy was obtained, and then a set of CT images was derived from the LAC map, which is at different but a known energy. Then, dual-energy subtraction was applied to remove metal artifacts. Results: Between the CT image sets of virtual high energy and the original, there is no significant difference in STD (standard deviation) (no more than 1.91%), while Merror (a parameter for quantification of the CT value differences between two images at the same position) varies from 58.83 to 101.6442. CNRs (Contrast-noise-ratio) in dual-energy subtracted CT images are 1.9% higher than those in the CT images processed by polar coordinate transformation. Conclusions: The Dual-energy subtraction is proved to be a better method for reduction of metal artifacts than the polar coordinate transformation scheme. Moreover, the dual-energy subtraction method is based on the reconstructed CT images obtained with a single energy CT scanner, which is more convenient for users not having access to the projection data.

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