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Research Papers

A Phantom Tissue System for the Calibration of Perfusion Measurements

[+] Author and Article Information
Ashvinikumar V. Mudaliar, Brent E. Ellis, Patricia L. Ricketts, Thomas E. Diller

 Virginia Tech—Wake Forest University School of Biomedical Engineering and Sciences; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061-0238

Otto I. Lanz

 Virginia Tech—Wake Forest University School of Biomedical Engineering and Sciences; Department of Small Animal Clinical Sciences, Virginia Tech, Blacksburg, VA 24061-0442

Elaine P. Scott

 Virginia Tech—Wake Forest University School of Biomedical Engineering and Sciences; Department of Mechanical Engineering, Virginia Tech; Department of Engineering, Seattle Pacific University, Seattle, WA 98119-1957

J Biomech Eng 130(5), 051002 (Jul 10, 2008) (10 pages) doi:10.1115/1.2948417 History: Received July 25, 2007; Revised January 19, 2008; Published July 10, 2008

A convenient method for testing and calibrating surface perfusion sensors has been developed. A phantom tissue model is used to simulate the nondirectional blood flow of tissue perfusion. A computational fluid dynamics (CFD) model was constructed in Fluent® to design the phantom tissue and validate the experimental results. The phantom perfusion system was used with a perfusion sensor based on clearance of thermal energy. A heat flux gage measures the heat flux response of tissue when a thermal event (convective cooling) is applied. The blood perfusion and contact resistance are estimated by a parameter estimation code. From the experimental and analytical results, it was concluded that the probe displayed good measurement repeatability and sensitivity. The experimental perfusion measurements in the tissue were in good agreement with those of the CFD models and demonstrated the value of the phantom tissue system.

Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

Convective perfusion probe (left: 3D model of the housing; right: actual probe with the sensor)

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Figure 2

Model based estimation method used to estimate the unknown parameter, blood perfusion, ω, and contact resistance, Rc

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Figure 3

Typical heat flux convergence plot from parameter estimation routine

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Figure 4

The basic concept for a controlled perfusion phantom tissue

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Figure 5

Flow near the surface of the heat flux gage in the phantom tissue

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Figure 6

Schematic of phantom tissue test stand and probe

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Figure 7

Photograph of the experimental test section setup.

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Figure 8

Schematic arrangement of the thermocouples in the test setup

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Figure 9

The Fluent axisymmetric model of the phantom tissue shown with boundary conditions, streamlines, and temperature distribution

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Figure 10

Heat flux response from the Fluent flow model

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Figure 11

Perfusion estimates for different thermal event durations

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Figure 12

Perfusion estimate comparison for Fluent 2D axisymmetric and 3D flow models

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Figure 13

Sensitivity analysis for blood perfusion and contact resistance

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Figure 14

Repeatability of the experimental heat flux for 15cm3∕min flow rate

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Figure 15

Heat flux sensitivity for the phantom tissue test stand

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Figure 16

Perfusion estimates of the experimental tests and the Fluent model

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Figure 17

Contact resistance estimates for the phantom tissue tests

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