Research Papers

Development and Validation of a Brain Phantom for Therapeutic Cooling Devices

[+] Author and Article Information
Ryan D. M. Packett

Department of Biomedical Engineering,
Wake Forest University,
575 N. Patterson Avenue Suite 120,
Winston-Salem, NC 27101
e-mail: rpackett@wakehealth.edu

Philip J. Brown

Department of Biomedical Engineering,
Wake Forest University,
575 N. Patterson Avenue Suite 120,
Winston-Salem, NC 27101
e-mail: phibrown@wakehealth.edu

Gautam S. S. Popli

Department of Neurology,
Wake Forest Baptist Medical Center,
Medical Center Boulevard,
Winston-Salem, NC 27104
e-mail: gpopli@wakehealth.edu

F. Scott Gayzik

Department of Biomedical Engineering,
Wake Forest University,
575 N. Patterson Avenue Suite 120,
Winston-Salem, NC 27101
e-mail: sgayzik@wakehealth.edu

1Corresponding author.

Manuscript received December 9, 2016; final manuscript received March 6, 2017; published online April 6, 2017. Assoc. Editor: Ram Devireddy.

J Biomech Eng 139(5), 051007 (Apr 06, 2017) (7 pages) Paper No: BIO-16-1506; doi: 10.1115/1.4036215 History: Received December 09, 2016; Revised March 06, 2017

Tissue cooling has been proven as a viable therapy for multiple conditions and injuries and has been applied to the brain to treat epilepsy and concussions, leading to improved long-term outcomes. To facilitate the study of temperature reduction as a function of various cooling methods, a thermal brain phantom was developed and analyzed. The phantom is composed of a potassium-neutralized, superabsorbent copolymer hydrogel. The phantom was tested in a series of cooling trials using a cooling block and 37 deg water representing nondirectional blood flow ranging up to 6 gph, a physiologically representative range based on the prototype volume. Results were compared against a validated finite difference (FD) model. Two sets of parameters were used in the FD model: one set to represent the phantom itself and a second set to represent brain parenchyma. The model was then used to calculate steady-state cooling at a depth of 5 mm for all flow rates, for both the phantom and a model of the brain. This effort was undertaken to (1) validate the FD model against the phantom results and (2) evaluate how similar the thermal response of the phantom is to that of a perfused brain. The FD phantom model showed good agreement with the empirical phantom results. Furthermore, the empirical phantom agreed with the predicted brain response within 3.5% at physiological flow, suggesting a biofidelic thermal response. The phantom will be used as a platform for future studies of thermally mediated therapies applied to the cerebral cortex.

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Fig. 3

Diagram of brain phantom test loop and cooling device test loop

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Fig. 4

The cooling device fixture controls six degrees of freedom of the device to ensure a repeatable placement method. A—CPU cooling block, B—cooling device fixture, C—foam insulation, D—cooling device inlet tube, E—cooling device outlet tube, and F—phantom container.

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Fig. 2

The inlet tubes (C) disperse the warm water evenly throughout the phantom. A—bulkhead inlet (x4), B—bulkhead drain (x4), C—inlet tubes (x2), D—internal thermocouples (x3), E—instrumentation umbilical, F—test fixture, and G—cooling device fixture.

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Fig. 1

Type L granular SAP in dry and wet stages, mm scale

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Fig. 8

Validation of FD model versus study by Yang et al. Empirical data from the 4 gph results from the phantom are included for reference.

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Fig. 5

Schematic representation of the one-dimensional mathematical model

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Fig. 6

The results of the “step-up” test show the effect that perfusion rate has on steady-state cooling

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Fig. 7

The bar graph shows that the brain phantom is able to match the predicted brain response, usually to within 1–2 deg



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