TY - JOUR
T1 - Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures
AU - Fahrenholtz, Samuel J.
AU - Stafford, R. Jason
AU - Maier, Florian
AU - Hazle, John D.
AU - Fuentes, David
N1 - Funding Information:
The research in this paper was supported in part through NIH grants 5T32CA119930-03, 1R21EB010196-01, CA016672, and TL1TR000369. Canine data was obtained from BioTex, under grants R43-CA79282, R44-CA79282, R43-AG19276. The authors would also like to thank the DAKOTA [67], ITK [74], Paraview [75], PETSc [18], libMesh [76], and CUBIT [66] communities for providing enabling software for scientific computation and visualisation. Simulations were performed using allocations at the Texas Advanced Computing Center. The authors alone are responsible for the content and writing of the paper.
PY - 2013/6
Y1 - 2013/6
N2 - Purpose: A generalised polynomial chaos (gPC) method is used to incorporate constitutive parameter uncertainties within the Pennes representation of bioheat transfer phenomena. The stochastic temperature predictions of the mathematical model are critically evaluated against MR thermometry data for planning MR-guided laser-induced thermal therapies (MRgLITT). Methods: The Pennes bioheat transfer model coupled with a diffusion theory approximation of laser tissue interaction was implemented as the underlying deterministic kernel. A probabilistic sensitivity study was used to identify parameters that provide the most variance in temperature output. Confidence intervals of the temperature predictions are compared to MR temperature imaging (MRTI) obtained during phantom and in vivo canine (n = 4) MRgLITT experiments. The gPC predictions were quantitatively compared to MRTI data using probabilistic linear and temporal profiles as well as 2-D 60 °C isotherms. Results: Optical parameters provided the highest variance in the model output (peak standard deviation: anisotropy 3.51 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.43 °C, and perfusion 0.94 °C). Further, within the statistical sense considered, a non-linear model of the temperature and damage-dependent perfusion, absorption, and scattering is captured within the confidence intervals of the linear gPC method. Multivariate stochastic model predictions using parameters with the dominant sensitivities show good agreement with experimental MRTI data. Conclusions: Given parameter uncertainties and mathematical modelling approximations of the Pennes bioheat model, the statistical framework demonstrates conservative estimates of the therapeutic heating and has potential for use as a computational prediction tool for thermal therapy planning.
AB - Purpose: A generalised polynomial chaos (gPC) method is used to incorporate constitutive parameter uncertainties within the Pennes representation of bioheat transfer phenomena. The stochastic temperature predictions of the mathematical model are critically evaluated against MR thermometry data for planning MR-guided laser-induced thermal therapies (MRgLITT). Methods: The Pennes bioheat transfer model coupled with a diffusion theory approximation of laser tissue interaction was implemented as the underlying deterministic kernel. A probabilistic sensitivity study was used to identify parameters that provide the most variance in temperature output. Confidence intervals of the temperature predictions are compared to MR temperature imaging (MRTI) obtained during phantom and in vivo canine (n = 4) MRgLITT experiments. The gPC predictions were quantitatively compared to MRTI data using probabilistic linear and temporal profiles as well as 2-D 60 °C isotherms. Results: Optical parameters provided the highest variance in the model output (peak standard deviation: anisotropy 3.51 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.43 °C, and perfusion 0.94 °C). Further, within the statistical sense considered, a non-linear model of the temperature and damage-dependent perfusion, absorption, and scattering is captured within the confidence intervals of the linear gPC method. Multivariate stochastic model predictions using parameters with the dominant sensitivities show good agreement with experimental MRTI data. Conclusions: Given parameter uncertainties and mathematical modelling approximations of the Pennes bioheat model, the statistical framework demonstrates conservative estimates of the therapeutic heating and has potential for use as a computational prediction tool for thermal therapy planning.
KW - Bioheat transfer
KW - Generalised polynomial chaos
KW - Laser tissue interaction
KW - Treatment planning
KW - Uncertainty quantification
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U2 - 10.3109/02656736.2013.798036
DO - 10.3109/02656736.2013.798036
M3 - Article
C2 - 23692295
AN - SCOPUS:84878853837
SN - 0265-6736
VL - 29
SP - 324
EP - 335
JO - International Journal of Hyperthermia
JF - International Journal of Hyperthermia
IS - 4
ER -