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Quantitative Mapping of Hemodynamics in the Lung, Brain, and Dorsal Window Chamber‐Grown Tumors Using a Novel, Automated Algorithm
Author(s) -
Fontanella Andrew N.,
Schroeder Thies,
Hochman Daryl W.,
Chen Raymond E.,
Hanna Gabi,
Haglund Michael M.,
Secomb Timothy W.,
Palmer Gregory M.,
Dewhirst Mark W.
Publication year - 2013
Publication title -
microcirculation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.793
H-Index - 83
eISSN - 1549-8719
pISSN - 1073-9688
DOI - 10.1111/micc.12072
Subject(s) - computer science , algorithm , pixel , microvessel , hemodynamics , blood flow , artificial intelligence , computer vision , medicine , immunohistochemistry
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.