On a mathematical model for laser-induced thermotherapy

Fasano, Antonio; Hömberg, Dietmar; Naumov, Dmitri

doi:10.1016/j.apm.2010.03.023

Cited by 56 publications

(46 citation statements)

References 7 publications

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“…Further sources of discrepancy between simulation and MRTI during the cooling regime, Figures 3C and 6, are not seen in studies with external optical fibers [71] and are likely due to inaccuracies of the Dirichlet boundary condition to model the convective cooling of the catheter. Validation of physics based models [72,73] of the convective heat transfer of the cooling fluid through the laser applicator is a topic of future work.…”

Section: Discussionmentioning

confidence: 99%

Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures

Fahrenholtz

Stafford

Maier

et al. 2013

International Journal of Hyperthermia

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Purpose-A generalized 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-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-Within the range of physically meaningful constitutive values relevant to the ablative temperature regime of MRgLITT, the sensitivity study indicated that the optical parameters, particularly the anisotropy factor, created the most variance in the stochastic model's output temperature prediction. Further, within the statistical sense considered, a nonlinear 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 modeling 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.

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Section: Discussionmentioning

confidence: 99%

Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures

Fahrenholtz

Stafford

Maier

et al. 2013

International Journal of Hyperthermia

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“…Square root filters [12] may be needed to improve the numerical stability of these higher complexity algorithms. Higher order modeling of the effect of the flow of the cooling fluid [45] within the laser applicator may also have additional benefit in better predicting the maximum temperature near the applicator for safety purposes. These techniques must be critically evaluated in terms of the desired clinical result and computational cost.…”

Section: Discussionmentioning

confidence: 99%

Kalman Filtered MR Temperature Imaging for Laser Induced Thermal Therapies

Fuentes

Yung

Hazle

et al. 2012

IEEE Trans. Med. Imaging

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The feasibility of using a stochastic form of Pennes bioheat model within a 3D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comparing predictions in these regions to the original measurements. Performance was quantitatively evaluated in terms of a dimensionless L2 (RMS) norm of the temperature error weighted by acquisition uncertainty. During periods of no data corruption, observed error histories demonstrate that the Kalman algorithm does not alter the high quality temperature measurement provided by MR thermal imaging. The KF-MRTI implementation considered is seen to predict the bioheat transfer with RMS error < 4 for a short period of time, Δt < 10sec, until the data corruption subsides. In its present form, the KF-MRTI method currently fails to compensate for consecutive for consecutive time periods of data loss Δt > 10sec.

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“…However, neither of these approaches is able to accurately capture the physics of the cooling fluid heated as thermal energy carried away from the delivery site via convection. The third approach is to model the cooling applicator explicitly and take into account the convective transport of the cooling fluid [38] in addition to add a convective term in Equation (1). However, this will increase the modelling complexity tremendously, especially for finite-element mesh generation near the applicator.…”

Section: Mathematical Modelling Aspectsmentioning

confidence: 99%

Model-based planning and real-time predictive control for laser-induced thermal therapy

Feng

Fuentes

2011

International Journal of Hyperthermia

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In this article, the major idea and mathematical aspects of model-based planning and real-time predictive control for laser-induced thermal therapy (LITT) are presented. In particular, a computational framework and its major components developed by authors in recent years are reviewed. The framework provides the backbone for not only treatment planning but also real-time surgical monitoring and control with a focus on MR thermometry enabled predictive control and applications to image-guided LITT, or MRgLITT. Although this computational framework is designed for LITT in treating prostate cancer, it is further applicable to other thermal therapies in focal lesions induced by radio-frequency (RF), microwave and high-intensity-focused ultrasound (HIFU). Moreover, the model-based dynamic closed-loop predictive control algorithms in the framework, facilitated by the coupling of mathematical modelling and computer simulation with real-time imaging feedback, has great potential to enable a novel methodology in thermal medicine. Such technology could dramatically increase treatment efficacy and reduce morbidity.

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On a mathematical model for laser-induced thermotherapy

Cited by 56 publications

References 7 publications

Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures

Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures

Kalman Filtered MR Temperature Imaging for Laser Induced Thermal Therapies

Model-based planning and real-time predictive control for laser-induced thermal therapy

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