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Proceedings Paper

OCT of living human kidney: case study
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Paper Abstract

Optical coherence tomography (OCT) is a useful optical biopsy tool. Its potential in the evaluation of living kidney has been demonstrated. One of such applications is to predict the acute tubular necrosis (ATN) associated with kidney transplantation. The light dense and lucent regions seen in 2D OCT scanning are considered as a useful marker of the renal tubules. In this study, the OCT examination of living human kidney was carried out using a swept source (SS) OCT (SS-OCT) system. The light lucent regions in the cortex obtained on the OCT scan were defined as low signal cavities. The structure features of characteristic cavities in 2D and simulated 3D OCT images were quantitatively analyzed using Amira and Matlab programs. Although the imaging acquisition and real-time analysis were feasible for the examination of donor kidney before and after the transplantation, as the imaging acquisition was obtained under the hand-hold fashion, OCT images might become blurred and the tubules became hardly distinguishable from cortex background, especially for 3D images. In order to optimize the scanning parameters of the OCT imaging process, the influence of the jittering of the living kidney on the quality of OCT imaging and the distortion of the renal tubule structure were studied.

Paper Details

Date Published: 5 November 2018
PDF: 6 pages
Proc. SPIE 10816, Advanced Optical Imaging Technologies, 108161R (5 November 2018); doi: 10.1117/12.2502655
Show Author Affiliations
Yuhong Fang, Fujian Normal Univ. (China)
Minnan Normal Univ. (China)
Wei Gong, Fujian Normal Univ. (China)
Junxia Li, Fuzhou General Hospital of Nanjing Military Command (China)
Jianming Tan, Fuzhou General Hospital of Nanjing Military Command (China)
Weijun Li, Fujian Normal Univ. (China)
Yongzeng Li, Fujian Normal Univ. (China)
Shusen Xie, Fujian Normal Univ. (China)
Zheng Huang, Fujian Normal Univ. (China)


Published in SPIE Proceedings Vol. 10816:
Advanced Optical Imaging Technologies
Xiao-Cong Yuan; Kebin Shi; Michael G. Somekh, Editor(s)

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