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Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy

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Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy

Auteurs : D. Fuentes ; Y. Feng ; A. Elliott ; A. Shetty ; R. J. Mcnichols ; J. T. Oden ; R. J. Stafford

Source :

IEEE transactions on bio-medical engineering [ 0018-9294 ] ; 2010.

RBID : PMC:3857613

Abstract

The treatment times of laser induced thermal therapies (LITT) guided by computational prediction are determined by the convergence behavior of PDE constrained optimization problems. In this work, we investigate the convergence behavior of a bioheat transfer constrained calibration problem to assess the feasibility of applying to real-time patient specific data. The calibration techniques utilize multi-planar thermal images obtained from the non-destructive in vivo heating of canine prostate. The calibration techniques attempt to adaptively recover the bio-thermal heterogeneities within the tissue on a patient specific level and results in a formidable PDE constrained optimization problem to be solved in real time. A comprehensive calibration study is performed with both homogeneous and spatially heterogeneous bio-thermal model parameters with and without constitutive nonlinearities. Initial results presented here indicate that the calibration problems involving the inverse solution of thousands of model parameters can converge to a solution within three minutes and decrease the ‖·‖L2(0,T;L2(Ω))2 norm of the difference between computational prediction and the measured temperature values to a patient specific regime.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857613

DOI: 10.1109/TBME.2009.2037733
PubMed: 20142153
PubMed Central: 3857613

Affiliations:

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PMC:3857613

Le document en format XML

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<front><div type="abstract" xml:lang="en"><p id="P1">The treatment times of laser induced thermal therapies (LITT) guided by computational prediction are determined by the convergence behavior of PDE constrained optimization problems. In this work, we investigate the convergence behavior of a bioheat transfer constrained calibration problem to assess the feasibility of applying to real-time patient specific data. The calibration techniques utilize multi-planar thermal images obtained from the non-destructive <italic>in vivo</italic> heating of canine prostate. The calibration techniques attempt to adaptively recover the bio-thermal heterogeneities within the tissue on a patient specific level and results in a formidable PDE constrained optimization problem to be solved in real time. A comprehensive calibration study is performed with both homogeneous and spatially heterogeneous bio-thermal model parameters with and without constitutive nonlinearities. Initial results presented here indicate that the calibration problems involving the inverse solution of thousands of model parameters can converge to a solution within three minutes and decrease the 
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Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy

Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy

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Abstract

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Le document en format XML

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Pour générer des pages wiki