Thermal response of human legs during cooling

Mitchell, Jason W.; Galvez, T. L.; Hengle, J.; Myers, G. E.; Siebecker, Karl L.

doi:10.1152/jappl.1970.29.6.859

Cited by 90 publications

(44 citation statements)

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“…(6) represents heat generated by metabolic process. Mitchell et al [42] observed that the metabolic heat generation is a function of local tissue temperature and is given by:…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Numerical simulation of dual-phase-lag bioheat transfer model during thermal therapy

Kumar

Rai

2016

Mathematical Biosciences

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“…(6) represents heat generated by metabolic process. Mitchell et al [42] observed that the metabolic heat generation is a function of local tissue temperature and is given by:…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Numerical simulation of dual-phase-lag bioheat transfer model during thermal therapy

Kumar

Rai

2016

Mathematical Biosciences

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“…Chao et al [24,25] investigated the steady and unsteady models with all the parameters as constant. Mitchell et al [26] developed an analytical model to predict temperature levels as function of time in human legs. Song et al [27] have developed a combined micro and macro vascular model for whole limb heat transfer.…”

Section: Dirichlet Condition (Constant Temperature)mentioning

confidence: 99%

“…After developing the mathematical model for temperature distribution in SST region of irregular tapered shaped human limbs, incorporated the values of the different parameters involved through various experimental papers [11,26]. Finite element technique has been used as in our previous paper [1] to solve the mathematical model, computed the results by using Matlab 7.5 and obtain the graphs to validate the results with the physiology of the upper limb.…”

Section: Dirichlet Condition (Constant Temperature)mentioning

confidence: 99%

On Some Probabilistic Aspects of Diffusion Models for Tissue Growth

Kiseľák¹,

Pardasani²,

Adlakha³

et al. 2013

TOSPJ

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Abstract:Understanding of tissue growth is in its nature multidisciplinary, since it varies from cancer diagnostics, image processing, fractal analysis to regular and non-regular heat flows. By medical sciences it was requested to better understood the tissue grow relation to mathematical modelling (stochastic geometry, fractal growth, diffusions). It is clear, that deterministic fractal is not an appropriate model for cancer growth. Stochastic fractal is more appropriate, however, a validation measure should be developed for better comparability with advanced stochastic geometry model, e.g. Quermass-interaction process. Moreover, relation temperature-geometry of the tissue is studied. We have partial results, where it is observed, that benign alterations and malignant tumors originating from glandular tissues (e.g.mammary, prostatic, pancreatic) are naturally modelled by non-standard diffusions. For standard diffusions, fair approximation is provided by analytical models based on convective heat transfer in infinite tissues volume (e.g. model given by Perl 1962, later extended by [1]).

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“…Despite its frequent application, there are few experimental evaluations of the Pennes BHTE , all relying upon a small number of discrete thermocouple or thermistor measurements. This paper extends previous efforts by using 3D MRTI to quantify

{\dot{Q}}_{normalb normall}

, determine the Pennes perfusion parameter w and test the predictive ability of the Pennes BHTE.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance temperature imaging‐based quantification of blood flow‐related energy losses

2015

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This study presents a new approach for evaluating bioheat transfer equation (BHTE) models used in treatment planning, control and evaluation of all thermal therapies. First, 3D magnetic resonance temperature imaging (MRTI) data are used to quantify blood flow-related energy losses, including the effects of perfusion and convection. Second, that information is used to calculate parameters of a BHTE model; in this paper the widely used Pennes BHTE. As a self-consistency check, the BHTE parameters are utilized to predict the temperatures from which they were initially derived. The approach is evaluated with finite-difference simulations and implemented experimentally with focused ultrasound heating of an ex vivo porcine kidney perfused at 0, 20 and 40 ml/min (n = 4 each). The simulation results demonstrate accurate quantification of blood flow-related energy losses, except in regions of sharp blood flow discontinuities where the transitions are spatially smoothed. The smoothed transitions propagate into estimates of the Pennes perfusion parameter but have limited effect on the accuracy of temperature predictions using those estimates. Longer acquisition time periods mitigate the effects of MRTI noise, but worsen the effect of flow discontinuities. For the no-flow kidney experiments, the estimates of a uniform, constant Pennes perfusion parameter are approximately zero, and at 20 and 40 ml/min, the average estimates increase with flow rate to 3.0 and 4.2 kg/m3/s, respectively. When Pennes perfusion parameter values are allowed to vary spatially, but remain temporally constant, BHTE temperature predictions are more accurate than when using spatially uniform, constant Pennes perfusion values, with reductions in RMSE values of up to 79%. Locations with large estimated perfusion values correspond to high flow regions of the kidney observed in T1-weighted MR images. This novel, MRTI-based technique holds promise for improving understanding of thermal therapy biophysics and for evaluating biothermal models.

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Thermal response of human legs during cooling

Cited by 90 publications

References 0 publications

Numerical simulation of dual-phase-lag bioheat transfer model during thermal therapy

Numerical simulation of dual-phase-lag bioheat transfer model during thermal therapy

On Some Probabilistic Aspects of Diffusion Models for Tissue Growth

Magnetic resonance temperature imaging‐based quantification of blood flow‐related energy losses

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