Dr. Shenghan Ren Profile

Dr. Shenghan Ren

at Xidian Univ

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Publications (2)

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Proceedings Article | 6 March 2023 Poster + Paper

Spectral unmixing for multispectral fluorescence imaging using prior knowledge of spectral signatures

Ruirui Zhang, Duofang Chen, Shenghan Ren, Feng Chen, Xueli Chen

Proceedings Volume 12371, 123710B (2023) https://doi.org/10.1117/12.2656012

KEYWORDS: Fluorescence, Autofluorescence, Multispectral imaging, Image segmentation, Matrices, Fluorophores, Fluorescence imaging, Detection and tracking algorithms, Signal to noise ratio, Image processing algorithms and systems

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Multispectral imaging is becoming a key technique for biomedical research, but the crosstalk between autofluorescence and fluorescent material severely affects the interpretation of fluorescence images. Spectral unmixing is an effective technique for removing autofluorescence and separating fluorescent targets in multispectral fluorescence imaging. However, the effectiveness of most methods of spectral unmixing has a strong relationship with the noise in the image. In this work, we propose a multispectral fluorescence unmixing method based on a priori information to obtain the pure spectra and their corresponding abundance coefficients in the images. First, the obtained multispectral image is segmented into several superpixels using a superpixel segmentation method, and then the relative pure spectra are extracted using a spectral extraction algorithm on the superpixels. Since the autofluorescence distribution is spread over the whole body, the extracted spectra in which the autofluorescence can be considered as pure spectra are used as a priori knowledge for unmixing. The pixel spectral data that are similar to the set of relatively pure spectra are selected as the pure spectral candidate set. Then the pure spectra can be obtained using the Non-negative matrix factorization method with prior knowledge(NMF-upk). Finally, the abundance corresponding to each spectral feature can be obtained through the least square method. The proposed unmixing method is tested on simulated data and the results show that our unmixing algorithm outperforms other methods.

Proceedings Article | 8 March 2019 Paper

Identification of angiogenesis and viable myocardium using hybrid cardiac imaging

Zhenzhen Xu, Bo Tao, Shenghan Ren, Feng Cao, Jimin Liang

Proceedings Volume 10951, 1095128 (2019) https://doi.org/10.1117/12.2512653

KEYWORDS: Image segmentation, Image fusion, Image registration, Heart, Single photon emission computed tomography, Positron emission tomography, Computed tomography, Cardiac imaging, Arteries, Image processing

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The existing hybrid cardiac imaging approaches focus on predicting the adverse cardiac events or disease diagnosis, yet do not offer any insight into the pathological advance in the repair process. Angiogenesis is one of the most important mechanism in the repair process after ischemic injury and has shown benefit to the prognosis of occlusive cardiovascular disorders, thus becomes a target of molecular therapies. In vivo monitoring of angiogenesis and comprehensive evaluation of cardiac function associated with angiogenesis are urgently needed in both research and clinical practice. In this paper, a multimodality image fusion strategy was proposed for angiogenesis and viable myocardium identification. Imaging approaches including coronary computed tomography angiography(CCTA), 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]DG) PET/CT, [⁶⁸Ga]-1,4,7-triazacyclononane-1,4,7-triacetic acid-(Arg-Gly-Asp)2 ([⁶⁸Ga]-NOTA-PRGD2) PET/CT and ^99mTc-sestamibi (^99mTc-MIBI) myocardial perfusion SPECT/CT scanning were performed to acquire both anatomy and three kinds of function information. All of these modality images were then fused by an automatic strategy consisting of ROI segmentation and cross modality registration. The left ventricle myocardium was categorized into 4 groups based on fusion result according to the respective relative tracer uptake. The final results intuitively reflected the extent of the [¹⁸F]DG and ^99mTc-MIBI uptake defect, the perfusion-metabolism mismatch area, as well as the location of the [⁶⁸Ga]-NOTA-PRGD2 signal. The hybrid CCTA-PET-SPECT image verified the occurrence of angiogenesis based on the in vivo noninvasive molecular imaging approaches and visualized the hibernating myocardium. The presented fusion strategy is helpful in facilitating the study of the relationship between viability, perfusion and blocked coronary arteries, as well as angiogenesis.

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