Quantitative mapping of hemodynamics in the lung, brain, and dorsal window chamber-grown tumors using a novel, automated algorithm.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Fontanella, AN; Schroeder, T; Hochman, DW; Chen, RE; Hanna, G; Haglund, MM; Rajaram, N; Frees, AE; Secomb, TW; Palmer, GM; Dewhirst, MW

Published Date

  • November 2013

Published In

Volume / Issue

  • 20 / 8

Start / End Page

  • 724 - 735

PubMed ID

  • 23781901

Pubmed Central ID

  • 23781901

Electronic International Standard Serial Number (EISSN)

  • 1549-8719

Digital Object Identifier (DOI)

  • 10.1111/micc.12072

Language

  • eng

Conference Location

  • United States