Dr. Xiao Zhang Profile

Dr. Xiao Zhang

at Beijing Institute of Technology

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Publications (2)

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Proceedings Article | 27 November 2023 Poster + Paper

Portable wide-filed OCT/OCTA for retinal imaging

Nian Xin, Haozhe Zhong, Xiao Zhang, Qin Li

Proceedings Volume 12770, 127702L (2023) https://doi.org/10.1117/12.2687262

KEYWORDS: Optical coherence tomography, Portability, Retinal scanning, Retina, Galvanometers, Blood vessels, Biological imaging, Tissues, Eye, Design and modelling

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Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have achieved great success in the field of ophthalmology and are essential in early screening and diagnosis. They could perform noninvasive structural and vascular imaging of the eye. However, most Chinese community hospitals do not have any OCT equipment, due to the high cost. Portable OCT equipment could provide mobile and shared medical diagnostic services as an ideal solution to above problem. Therefore, we design and demonstrate a portable SD-OCT/OCTA system for retinal imaging with A-scan rate of 120KHz, based on a broadband light source with center wavelength of 850nm and bandwidth of ~100nm. The proposed equipment, consists of a laptop and an OCT engine with 30×25×20cm3 size and ~5kg weight, could be carried by one person from a hospital to another. The portable system offers an axial resolution of ~3.8 μm, a transverse resolution of ~7 μm on retina and a wide field of view of ~60 degrees. The system parameters of scanning unit have been simulated and optimized using software to achieve focusing spot with diffraction limited size on retina. In a word, the proposed OCT/OCTA equipment offers a nice tradeoff between imaging performance and portability, therefore makes up for the shortcomings of traditional OCT and is hopeful to enable the sharing economy for OCT.

Proceedings Article | 19 December 2022 Presentation + Paper

Application of IVOCT to coronary atherosclerotic plaque

Yukang Wei, Xingkang Lin, Mingliang Zheng, Xiao Zhang, Qin Li

Proceedings Volume 12320, 123200O (2022) https://doi.org/10.1117/12.2654764

KEYWORDS: Image segmentation, Intelligence systems, Optical coherence tomography, Machine learning, Imaging systems, Image resolution, Image classification, Heart, Feature extraction, Detection and tracking algorithms

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Coronary atherosclerotic heart disease is one of the main causes of death from cardiovascular diseases. Early detection of atherosclerotic lesions can help clinicians understand the condition of cardiovascular patients and provide reference for better treatment measures. Compared with other detection technologies, intravascular optical coherence tomography (IVOCT) has the advantages of no radiation, high resolution, and high imaging speed. Therefore, it plays an important role in the detection and evaluation of atherosclerotic plaque. Although IVOCT has been widely used in the detection of plaque in coronary vessels, the imaging system could not directly provide effective plaque feature identification information, and clinicians can only judge the characteristics of plaque according to their own experience. Based on a brief introduction of the application of IVOCT in detecting coronary atherosclerotic plaque, this paper introduces the method of eliminating the vascular motion artifact caused by cardiac pulsation. The automatic segmentation and classification of IVOCT images are studied by using machine learning method. And the plaque features of calcified plaque, lipid plaque and fibrous plaque in IVOCT images are extracted. The deep learning algorithm is used to analyze the characteristics of vulnerable plaque and put forward quantitative evaluation indicators. It is very important to develop the intelligent recognition system of IVOCT in plaque type, that provide objective, intuitive and accurate plaque classification marks, display and rupture risk assessment for the clinic. So that clinicians can get rid of the current situation of relying solely on experience for lesion recognition, and save patients' lives in time.

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