Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2016, Vol. 27 ›› Issue (5): 125 doi: 10.1007/s41365-016-0121-7


Quantitative analysis of 3D vasculature for evaluation of angiogenesis in liver fibrosis with SR-μCT

Hai Tan 1,2 , Yi Fu 3,4 , Da-Dong Wang 5 , Xi Zhang 3,4 , Ti-Qiao Xiao 1,2   

  1. 1 Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading Campus, Shanghai 201800, China
    2 University of Chinese Academy of Sciences, Beijing 100049, China
    3 Department of Radiology, Cancer Hospital, Fudan University, Shanghai 200032, China
    4 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
    5 Quantitative Imaging, CSIRO Data61, Marsfield, NSW 2122, Australia
  • Contact: Ti-Qiao Xiao
  • Supported by:

    This work is supported by the National Basic Research Program of China (No. 2010CB834301), CAS-CSIRO Collaborative Research Project (GJHZ1303), the Shanghai Municipal Natural Science Foundation (No. 11ZR1407800), and the Joint Funds of the National Natural Science Foundation of China (Nos. U1232205, 81430087 and 81271574).

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Hai Tan, Yi Fu, Da-Dong Wang, Xi Zhang, Ti-Qiao Xiao. Quantitative analysis of 3D vasculature for evaluation of angiogenesis in liver fibrosis with SR-μCT.Nuclear Science and Techniques, 2016, 27(5): 125     doi: 10.1007/s41365-016-0121-7


The micro-CT imaging of vasculature is a powerful tool for evaluation of angiogenesis, a prominent characteristic of hepatic fibrosis. The segment or bifurcation density, which is usually adopted to evaluate the degree of hepatic fibrosis, does not always work and may lead to incorrect assessment, especially when the threedimensional vasculature obtained is imperfect in sample preparation or image collection. In this paper, we propose a new parameter to solve this problem. The experimental results demonstrate that the method is robust and reliable, and is practical for angiogenesis evaluation, despite of image data imperfections. This quantitative analysis method can be extended to investigate other kinds of diseases in which vasculature change is a key indicator.

Key words: SR-lCT imaging, 3D image processing, Quantitative analysis, 3D vasculature, Angiogenesis