Quantitative Mapping of Hemodynamics in the Lung, Brain, and Dorsal Window Chamber-Grown Tumors Using a Novel, Automated Algorithm

Andrew N. Fontanella, Thies Schroeder, Daryl W. Hochman, Raymond E. Chen, Gabi Hanna, Michael M. Haglund, Timothy W. Secomb, Gregory M. Palmer, Mark W. Dewhirst

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Objective: Hemodynamic properties of vascular beds are of great interest in a variety of clinical and laboratory settings. However, there presently exists no automated, accurate, technically simple method for generating blood velocity maps of complex microvessel networks. Methods: Here, we present a novel algorithm that addresses the problem of acquiring quantitative maps by applying pixel-by-pixel cross-correlation to video data. Temporal signals at every spatial coordinate are compared with signals at neighboring points, generating a series of correlation maps from which speed and direction are calculated. User-assisted definition of vessel geometries is not required, and sequential data are analyzed automatically, without user bias. Results: Velocity measurements were validated against the dual-slit method and against in vitro capillary flow with known velocities. The algorithm was tested in three different biological models in order to demonstrate its versatility. Conclusions: The hemodynamic maps presented here demonstrate an accurate, quantitative method of analyzing dynamic vascular systems.

Original languageEnglish (US)
Pages (from-to)724-735
Number of pages12
JournalMicrocirculation
Volume20
Issue number8
DOIs
StatePublished - Nov 1 2013

Keywords

  • Blood flow
  • Computational
  • Image processing
  • Tumor microcirculation

ASJC Scopus subject areas

  • Physiology
  • Molecular Biology
  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)

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