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Research Papers

New Mathematical Model to Estimate Tissue Blood Perfusion, Thermal Contact Resistance and Core Temperature

[+] Author and Article Information
Abdusalam Alkhwaji, Brian Vick, Tom Diller

 Mechanical Engineering Department, Virginia Tech, Blacksburg, VA 24061-0238

J Biomech Eng 134(8), 081004 (Aug 06, 2012) (8 pages) doi:10.1115/1.4007093 History: Received December 06, 2011; Accepted June 13, 2012; Revised June 13, 2012; Posted July 06, 2012; Published August 06, 2012; Online August 06, 2012

Analytical solutions were developed based on the Green’s function method to describe heat transfer in tissue including the effects of blood perfusion. These one-dimensional transient solutions were used with a simple parameter estimation technique and experimental measurements of temperature and heat flux at the surface of simulated tissue. It was demonstrated how such surface measurements can be used during step changes in the surface thermal conditions to estimate the value of three important parameters: blood perfusion (wb ), thermal contact resistance (R″), and core temperature of the tissue (Tcore ). The new models were tested against finite-difference solutions of thermal events on the surface to show the validity of the analytical solution. Simulated data was used to demonstrate the response of the model in predicting optimal parameters from noisy temperature and heat flux measurements. Finally, the analytical model and simple parameter estimation routine were used with actual experimental data from perfusion in phantom tissue. The model was shown to provide a very good match with the data curves. This demonstrated the first time that all three of these important parameters (wb , R″, and Tcore ) have simultaneously been estimated from a single set of thermal measurements at the surface of tissue.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 3

Measured temperature from a thermal event

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Figure 4

Initial steady-state temperature distributions

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Figure 6

Simulated two-dimensional effects on the sensor heat flux

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Figure 7

Search process for the optimal estimated parameters.

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Figure 8

Illustration of one sequence of iterations for optimal parameters (a) Blood perfusion (b) Thermal contact resistance

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Figure 9

Visualization of the two-dimensional optimization problem

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Figure 10

Analytical results for 1% noise added to measured temperature (a) and heat flux (b)

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Figure 11

Measured and analytical skin temperature (a) and heat flux (b) from the experiment for 15 CC/min flow rate

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Figure 1

Combination heat flux/temperature sensor

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Figure 5

Simulated temperature (a) and heat flux (b) for the analytical and finite-difference models

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