David Fuentes

University of Texas MD Anderson Cancer Center, Houston, Texas, United States

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Publications (25)40.48 Total impact

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    ABSTRACT: PurposeSeveral methods in MRI use the phase information of the complex signal and require phase unwrapping (e.g., B0 field mapping, chemical shift imaging, and velocity measurements). In this work, an algorithm was developed focusing on the needs and requirements of MR temperature imaging applications.Methods The proposed method performs fully automatic unwrapping using a list of all pixels sorted by magnitude in descending order and creates and merges clusters of unwrapped pixels until the entire image is unwrapped. The algorithm was evaluated using simulated phantom data and in vivo clinical temperature imaging data.ResultsThe evaluation of the phantom data demonstrated no errors in regions with signal-to-noise ratios of at least 4.5. For the in vivo data, the algorithm did not fail at an average of more than one pixel for signal-to-noise ratios greater than 6.3. Processing times less than 30 ms per image were achieved by unwrapping pixels inside a region of interest (53 × 53 pixels) used for referenceless MR temperature imaging.Conclusions The algorithm has been demonstrated to operate robustly with clinical in vivo data in this study. The processing time for common regions of interest in referenceless MR temperature imaging allows for online updates of temperature maps without noticeable delay. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.
    Magnetic Resonance in Medicine 05/2014; · 3.27 Impact Factor
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    ABSTRACT: Abstract Purpose: Optically activated nanoparticle-mediated heating for thermal therapy applications is an area of intense research. The ability to characterise the spatio-temporal heating potential of these particles for use in modelling under various exposure conditions can aid in the exploration of new approaches for therapy as well as more quantitative prospective approaches to treatment planning. The purpose of this research was to investigate an inverse solution to the heat equation using magnetic resonance temperature imaging (MRTI) feedback, for providing optical characterisation of two types of nanoparticles (gold-silica nanoshells and gold nanorods). Methods: The optical absorption of homogeneous nanoparticle-agar mixtures was measured during exposure to an 808 nm laser using real-time MRTI. A coupled finite element solution of heat transfer was registered with the data and used to solve the inverse problem. The L2 norm of the difference between the temperature increase in the model and MRTI was minimised using a pattern search algorithm by varying the absorption coefficient of the mixture. Results: Absorption fractions were within 10% of literature values for similar nanoparticles. Comparison of temporal and spatial profiles demonstrated good qualitative agreement between the model and the MRTI. The weighted root mean square error was <1.5 σMRTI and the average Dice similarity coefficient for ΔT = 5 °C isotherms was >0.9 over the measured time interval. Conclusion: This research demonstrates the feasibility of using an indirect method for making minimally invasive estimates of nanoparticle absorption that might be expanded to analyse a variety of geometries and particles of interest.
    International Journal of Hyperthermia 12/2013; · 2.59 Impact Factor
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    ABSTRACT: Abstract 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.
    International Journal of Hyperthermia 05/2013; · 2.59 Impact Factor
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    ABSTRACT: Quantification of local variations in the optical properties of tumor tissue introduced by the presence of gold-silica nanoparticles (NP) presents significant opportunities in monitoring and control of NP-mediated laser induced thermal therapy (LITT) procedures. Finite element methods of inverse parameter recovery constrained by a Pennes bioheat transfer model were applied to estimate the optical parameters. Magnetic resonance temperature imaging (MRTI) acquired during a NP-mediated LITT of a canine transmissible venereal tumor in brain was used in the presented statistical inverse problem formulation. The maximum likelihood (ML) value of the optical parameters illustrated a marked change in the periphery of the tumor corresponding with the expected location of NP and area of selective heating observed on MRTI. Parameter recovery information became increasingly difficult to infer in distal regions of tissue where photon fluence had been significantly attenuated. Finite element temperature predictions using the ML parameter values obtained from the solution of the inverse problem are able to reproduce the NP selective heating within 5 °C of measured MRTI estimations along selected temperature profiles. Results indicate the ML solution found is able to sufficiently reproduce the selectivity of the NP mediated laser induced heating and therefore the ML solution is likely to return useful optical parameters within the region of significant laser fluence.
    Annals of Biomedical Engineering 08/2012; · 3.23 Impact Factor
  • J Yung, D Fuentes, J Hazle, R Stafford
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    ABSTRACT: Purpose: The proton resonance frequency (PRF) shift method is the most widely accepted method for magnetic resonance thermal imaging to provide real-time treatment monitoring of thermal therapies. However, the PRF shift technique involves the subtraction of a reference phase map, which causes the technique to be easily perturbed by tissue motion and other background contaminations. In this study, a three-dimensional background phase is estimated in order to create a phase reference for each time point. Methods: A magnetic resonance spectroscopy (MRS) sphere was scanned within a 3T MRI scanner employing a 3D fast SPGR sequence. Real and imaginary images were acquired to obtain phase images as the control. The ability to predict the background phase was investigated by systematically removing phase information from the control data set. Data was initially removed from a spherical region of interest (ROI) to simulate a region where ablativeheating would take place. In a second case, the same spherical ROI was removed as well as every other slice to further reduce the amount of existing data. A 3D finite element model was implemented to solve the Dirichlet problem given a measured phase on the boundary of the simulated available data. Results: Line profiles taken through the phantom indicate phase estimates to compare well with actual phase measurements. The phase estimation still shows good agreement when reducing the amount of data to every other slice. Conclusions: The 3D multi-slice temperature estimate potentially provides a robust technique that is not as susceptible to through-plane or in-plane motion-induced temperature artifacts as compared to thecurrent PRF shift method. The research in this paper was supported in part through 1R21EB010196-01.
    Medical Physics 06/2012; 39(6):3664. · 2.91 Impact Factor
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    ABSTRACT: Purpose: Magnetic resonance-guided laser induced thermal therapy (MRgLITT) is a minimally invasive thermal treatment for metastatic brain lesions, offering an alternative to conventional surgery. The purpose of this investigation is to incorporate uncertainty quantification (UQ) into the biothermal parameters used in the Pennes bioheat transfer equation (BHT), in order to account for imprecise values available in the literature. The BHT is a partial differential equation commonly used in thermal therapy models. Methods: MRgLITT was performed on an in vivo canine brain in a previous investigation. The canine MRgLITT was modeled using the BHT. The BHT has four parameters'" microperfusion, conductivity, optical absorption, and optical scattering'"which lack precise measurements in living brain and tumor. The uncertainties in the parameters were expressed as probability distribution functions derived from literature values. A univariate generalized polynomial chaos (gPC) expansion was applied to the stochastic BHT. The gPC approach to UQ provides a novel methodology to calculate spatio-temporal voxel-wise means and variances of the predicted temperature distributions. The performance of the gPC predictions were evaluated retrospectively by comparison with MR thermal imaging (MRTI) acquired during the MRgLITT procedure in the canine model. The comparison was evaluated with root mean square difference (RMSD), isotherm contours, spatial profiles, and z-tests. Results: The peak RMSD was ∼1.5 standard deviations for microperfusion, conductivity, and optical absorption, while optical scattering was ∼2.2 standard deviations. Isotherm contours and spatial profiles of the simulation's predicted mean plus or minus two standard deviations demonstrate the MRTI temperature was enclosed by the model's isotherm confidence interval predictions. An a = 0.01 z-test demonstrates agreement. Conclusions: The application of gPC for UQ is a potentially powerful means for providing predictive simulations despite poorly known input parameters. gPC provides an output that represents the probable distribution of outcomes for MRgLITT.
    Medical Physics 06/2012; 39(6):3857. · 2.91 Impact Factor
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    ABSTRACT: The feasibility of using a stochastic form of Pennes bioheat model within a 3-D 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 L(2) (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, ∆t < 10 s, 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 ∆t > 10 sec.
    IEEE transactions on medical imaging. 12/2011; 31(4):984-94.
  • Yusheng Feng, D. Fuentes
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    ABSTRACT: This article presents an over view on real-time predictive control for laser surgery based on the computational framework that consists of components for numerical implementation of the nonlinear heterogeneous Pennes equation of bioheat transfer including model calibration, remote data transfer, model coregistration, finite element meshing and parallel solution algorithms, cellular damage prediction, and optimal laser control. The goal is to develop a predictive computational tool that may be used by surgeons during a minimally invasive hyper/ hypothermia procedure to destroy cancerous tumors. The tool includes various components of computer models in the computational framework that controls the thermal source and makes a prediction of the treatment outcomes. Simultaneously, model parameters are updated to increase the accuracy based on the real-time intraoperative imaging data from in vivo temperature measurement. Current results show that it is important to consider the heterogeneity in the patient-specific cancerous region and the surrounding domain in order to the accuracy of prediction. By solving the corresponding inverse problem, predicted results can be improved by the experimental data, and capture well-known behavior of decreased perfusion in the damage region and hyperperfusion surrounding the damage region.
    IEEE Signal Processing Magazine 06/2011; · 3.37 Impact Factor
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    ABSTRACT: Magnetic resonance-guided laser-induced thermal therapy (MRgLITT) is currently undergoing initial safety and feasibility clinical studies for the treatment of intracranial lesions in humans. As studies progress towards evaluation of treatment efficacy, predictive computational models may play an important role for prospective 3D treatment planning. The current work critically evaluates a computational model of laser induced bioheat transfer against retrospective multiplanar MR thermal imaging (MRTI) in a canine model of the MRgLITT procedure in the brain. A 3D finite element model of the bioheat transfer that couples Pennes equation to a diffusion theory approximation of light transport in tissue is used. The laser source is modelled conformal with the applicator geometry. Dirichlet boundary conditions are used to model the temperature of the actively cooled catheter. The MRgLITT procedure was performed on n = 4 canines using a 1-cm diffusing tip 15-W diode laser (980 nm). A weighted L₂norm is used as the metric of comparison between the spatiotemporal MR-derived temperature estimates and model prediction. The normalised error history between the computational models and MRTI was within 1-4 standard deviations of MRTI noise. Active cooling models indicate that the applicator temperature has a strong effect on the maximum temperature reached, but does not significantly decrease the tissue temperature away from the active tip. Results demonstrate the computational model of the bioheat transfer may provide a reasonable approximation of the laser-tissue interaction, which could be useful for treatment planning, but cannot readily replace MR temperature imaging in a complex environment such as the brain.
    International Journal of Hyperthermia 01/2011; 27(5):453-64. · 2.59 Impact Factor
  • Yusheng Feng, David Fuentes
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    ABSTRACT: 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.
    International Journal of Hyperthermia 01/2011; 27(8):751-61. · 2.59 Impact Factor
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    ABSTRACT: To evaluate the accuracy of computer simulation in predicting the thermal damage region produced by a radiofrequency (RF) ablation procedure in an in vitro perfused bovine liver model. The thermal dose end point in the liver model is used to assess quantitatively computer prediction for use in prospective treatment planning of RF ablation procedures. Geometric details of the tri-cooled tip electrode were modeled. The resistive heating of a pulsed voltage delivery was simulated in four dimensions using finite element models (FEM) implemented on high-performance parallel computing architectures. A range of physically realistic blood perfusion parameters, 3.6-53.6 kg/sec/m(3), was considered in the computer model. An Arrhenius damage model was used to predict the thermal dose. Dice similarity coefficients (DSC) were the metric of comparison between computational predictions and T1-weighted contrast-enhanced images of the damage obtained from a RF procedure performed on an in vitro perfused bovine liver model. For a perfusion parameter greater than 16.3 kg/sec/m(3), simulations predict the temporal evolution of the damaged volume is perfusion limited and will reach a maximum value. Over a range of physically meaningful perfusion values, 16.3-33.1 kg/sec/m(3), the predicted thermal dose reaches the maximum damage volume within 2 minutes of the delivery and is in good agreement (DSC > 0.7) with experimental measurements obtained from the perfused liver model. As measured by the computed volumetric DSC, computer prediction accuracy of the thermal dose shows good correlation with ablation lesions measured in vitro in perfused bovine liver models over a range of physically realistic perfusion values.
    Journal of vascular and interventional radiology: JVIR 10/2010; 21(11):1725-32. · 1.81 Impact Factor
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    ABSTRACT: The treatment times of laser induced thermal therapies (LITT) guided by computational prediction are determined by the convergence behavior of partial differential equation (PDE)-constrained optimization problems. In this paper, 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 multiplanar thermal images obtained from the nondestructive in vivo heating of canine prostate. The calibration techniques attempt to adaptively recover the biothermal 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 biothermal 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 ||??||<sub>L</sub> <sub>2</sub> <sup>2</sup> <sub>(0,T;L</sub> <sub>2</sub> <sub>(??))</sub> norm of the difference between computational prediction and the measured temperature values to a patient-specific regime.
    IEEE Transactions on Biomedical Engineering 06/2010; · 2.35 Impact Factor
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    ABSTRACT: Image-guided ablation of tumors is assuming an increasingly important role in many oncology services as a minimally invasive alternative to conventional surgical interventions for patients who are not good candidates for surgery. Laser-induced thermal therapy (LITT) is a percutaneous tumor-ablation technique that utilizes high-power lasers placed interstitially in the tumor to deliver therapy. Multiple laser fibers can be placed into the treatment volume and, unlike other interstitial heating techniques, can be fired simultaneously to rapidly treat large volumes of tissue. Modern systems utilize small, compact, high-power laser diode systems with actively cooled applicators to help keep tissue from charring during procedures. Additionally, because this approach to thermal therapy is easily made magnetic resonance (MR) compatible, the incorporation of magnetic resonance imaging (MRI) for treatment planning, targeting, monitoring, and verification has helped to expand the number of applications in which LITT can be applied safely and effectively. We provide an overview of the clinically used technology and algorithms that provide the foundations for current state-of-the-art MR-guided LITT (MRgLITT), including procedures in the brain, liver, bone, and prostate as examples. In addition to advances in imaging and delivery, such as the incorporation of nanotechnology, next-generation MRgLITT systems are anticipated to incorporate an increasing presence of in silico-based modeling of MRgLITT procedures to provide human-assisted computational tools for planning, MR model-assisted temperature monitoring, thermal-dose assessment, and optimal control.
    Critical Reviews in Biomedical Engineering 01/2010; 38(1):79-100.
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    03/2009;
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    ABSTRACT: An adaptive feedback control system is presented which employs a computational model of bioheat transfer in living tissue to guide, in real-time, laser treatments of prostate cancer monitored by magnetic resonance thermal imaging. The system is built on what can be referred to as cyberinfrastructure-a complex structure of high-speed network, large-scale parallel computing devices, laser optics, imaging, visualizations, inverse-analysis algorithms, mesh generation, and control systems that guide laser therapy to optimally control the ablation of cancerous tissue. The computational system has been successfully tested on in vivo, canine prostate. Over the course of an 18 min laser-induced thermal therapy performed at M.D. Anderson Cancer Center (MDACC) in Houston, Texas, the computational models were calibrated to intra-operative real-time thermal imaging treatment data and the calibrated models controlled the bioheat transfer to within 5 degrees C of the predetermined treatment plan. The computational arena is in Austin, Texas and managed at the Institute for Computational Engineering and Sciences (ICES). The system is designed to control the bioheat transfer remotely while simultaneously providing real-time remote visualization of the on-going treatment. Post-operative histology of the canine prostate reveal that the damage region was within the targeted 1.2 cm diameter treatment objective.
    Annals of Biomedical Engineering 02/2009; 37(4):763-82. · 3.23 Impact Factor
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    ABSTRACT: In this paper, we present a model-based predictive control system that is capable of capturing physical and biological variations of laser-tissue interaction as well as heterogeneity in real-time during laser induced thermal therapy (LITT). Using a three-dimensional predictive bioheat transfer model, which is built based on regular magnetic resonance imaging (MRI) anatomic scan and driven by imaging data produced by real-time magnetic resonance temperature imaging (MRTI), the computational system provides a regirous real-time predictive control during surgical operation process. The unique feature of the this system is its ability for predictive control based on validated model with high precision in real-time, which is made possible by implementation of efficient parallel algorithms. The major components of the current computational systems involves real-time finite element solution of the bioheat transfer induced by laser-tissue interaction, solution module of real-time calibration problem, optimal laser source control, goal-oriented error estimation applied to the bioheat transfer equation, and state-of-the-art imaging process module to characterize the heterogeneous biological domain. The system was tested in vivo in a canine animal model in which an interstitial laser probe was placed in the prostate region and the desired treatment outcome in terms of ablation temperature and damage zone were achieved. Using the guidance of the predictive model driven by real-time MRTI data while applying the optimized laser heat source has the potential to provide unprecedented control over the treatment outcome for laser ablation.
    Proc SPIE 01/2009;
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    ABSTRACT: Predicting the outcome of thermotherapies in cancer treatment requires an accurate characterization of the bioheat transfer processes in soft tissues. Due to the biological and structural complexity of tumor (soft tissue) composition and vasculature, it is often very difficult to obtain reliable tissue properties that is one of the key factors for the accurate treatment outcome prediction. Efficient algorithms employing in vivo thermal measurements to determine heterogeneous thermal tissues properties in conjunction with a detailed sensitivity analysis can produce essential information for model development and optimal control. The goals of this paper are to present a general formulation of the bioheat transfer equation for heterogeneous soft tissues, review models and algorithms developed for cell damage, heat shock proteins, and soft tissues with nanoparticle inclusion, and demonstrate an overall computational strategy for developing a laser treatment framework with the ability to perform real-time robust calibrations and optimal control. This computational strategy can be applied to other thermotherapies using the heat source such as radio frequency or high intensity focused ultrasound.
    Computer Methods in Applied Mechanics and Engineering 01/2009; 198(21):1742-1750. · 2.62 Impact Factor
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    ABSTRACT: Laser surgery, or laser-induced thermal therapy, is a minimally invasive alternative or adjuvant to surgical resection in treating tumors embedded in vital organs with poorly defined boundaries. Its use, however, is limited due to the lack of precise control of heating and slow rate of thermal diffusion in the tissue. Nanoparticles, such as nanoshells, can act as intense heat absorbers when they are injected into tumors. These nanoshells can enhance thermal energy deposition into target regions to improve the ability for destroying larger cancerous tissue volumes with lower thermal doses. The goal of this paper is to present an integrated computer model using a so-called nested-block optimization algorithm to simulate laser surgery and provide transient temperature field predictions. In particular, this algorithm aims to capture changes in optical and thermal properties due to nanoshell inclusion and tissue property variation during laser surgery. Numerical results show that this model is able to characterize variation of tissue properties for laser surgical procedures and predict transient temperature fields comparable to those measured by in vivo magnetic resonance temperature imaging techniques. Note that the computational approach presented in the study is quite general and can be applied to other types of nanoparticle inclusions.
    Engineering With Computers 01/2009; 25(1):3-13. · 0.60 Impact Factor
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    ABSTRACT: For cancerous tumors in vital internal organs, minimally invasive laser surgery may be a desirable choice for cancer treatment due to its precise control and compatibility with most of the imaging modalities such as MRI (magnetic resonance imaging). However, the complexity of tumor composition and tissue response to a thermal dose demands real time optimization and control. In the previous work, we have developed a quite general computational framework that is capable of processing MRI anatomical data, providing pretreatment surgical protocol, and controlling tissue damage based on in vivo MRTI (magnetic resonance thermal imaging) data. In this paper, we describe computational techniques that are involved in real time optimization and control for laser surgical protocols of cancer treatment.
    BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on; 06/2008
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    ABSTRACT: Elevating the temperature of cancerous cells is known to increase their susceptibility to subsequent radiation or chemotherapy treatments, and in the case in which a tumor exists as a well-defined region, higher intensity heat sources may be used to ablate the tissue. These facts are the basis for hyperthermia based cancer treatments. Of the many available modalities for delivering the heat source, the application of a laser heat source under the guidance of real-time treatment data has the potential to provide unprecedented control over the outcome of the treatment process [7, 18]. The goals of this work are to provide a precise mathematical framework for the real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied to the equations of bioheat transfer and demonstrate that current finite element technology, parallel computer architecture, data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within the biological domain.
    Numerical Methods for Partial Differential Equations 04/2007; 23(4):904-922. · 1.21 Impact Factor

Publication Stats

134 Citations
40.48 Total Impact Points

Institutions

  • 2010–2014
    • University of Texas MD Anderson Cancer Center
      • Department of Imaging Physics
      Houston, Texas, United States
  • 2011
    • University of Houston
      Houston, Texas, United States
    • University of Texas at San Antonio
      • Department of Mechanical Engineering
      San Antonio, TX, United States
  • 2006–2009
    • University of Texas at Austin
      • Institute for Computational Engineering and Sciences
      Texas City, TX, United States
    • University of Alabama at Birmingham
      • Department of Mechanical Engineering
      Birmingham, Alabama, United States