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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011

Volume 6893 of the series Lecture Notes in Computer Science pp 528-536

Vessel Connectivity Using Murray’s Hypothesis

  • Yifeng JiangAffiliated withDepartment of Diagnostic Radiology, Yale University
  • , Zhen W. ZhuangAffiliated withDepartment of Internal Medicine Cardiology, Yale University
  • , Albert J. SinusasAffiliated withDepartment of Diagnostic Radiology, Yale UniversityDepartment of Internal Medicine Cardiology, Yale University
  • , Lawrence H. StaibAffiliated withDepartment of Diagnostic Radiology, Yale UniversityDepartment of Biomedical Engineering, Yale UniversityDepartment of Electrical Engineering, Yale University
  • , Xenophon PapademetrisAffiliated withDepartment of Diagnostic Radiology, Yale UniversityDepartment of Biomedical Engineering, Yale University

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Abstract

We describe a new method for vascular image analysis that incorporates a generic physiological principle to estimate vessel connectivity, which is a key issue in reconstructing complete vascular trees from image data. We follow Murray’s hypothesis of the minimum work principle to formulate the problem as an optimization problem. This principle reflects a global property of any vascular network, in contrast to various local geometric properties adopted as constraints previously. We demonstrate the effectiveness of our method using a set of microCT mouse coronary images. It is shown that the performance of our method has a statistically significant improvement over the widely adopted minimum spanning tree methods that rely on local geometric constraints.