David Fuentes

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

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Publications (33)62.2 Total impact

  • Archives of Computational Methods in Engineering 01/2015; · 4.14 Impact Factor
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    ABSTRACT: Morphometry techniques were applied to quantify the normal tissue therapy response in patients receiving whole-brain radiation for intracranial malignancies. Pre- and Post-irradiation magnetic resonance imaging (MRI) data sets were retrospectively analyzed in N = 15 patients. Volume changes with respect to pre-irradiation were quantitatively measured in the cerebrum and ventricles. Measurements were correlated with the time interval from irradiation. Criteria for inclusion included craniospinal irradiation, pre-irradiation MRI, at least one follow-up MRI, and no disease progression. The brain on each image was segmented to remove the skull and registered to the initial pre-treatment scan. Average volume changes were measured using morphometry analysis of the deformation Jacobian and direct template registration-based segmentation of brain structures. An average cerebral volume atrophy of [Formula: see text]0.2 and [Formula: see text]3 % was measured for the deformation morphometry and direct segmentation methods, respectively. An average ventricle volume dilation of 21 and 20 % was measured for the deformation morphometry and direct segmentation methods, respectively. The presented study has developed an image processing pipeline for morphometric monitoring of brain tissue volume changes as a response to radiation therapy. Results indicate that quantitative morphometric monitoring is feasible and may provide additional information in assessing response.
    International Journal of Computer Assisted Radiology and Surgery 11/2014; 41(6). DOI:10.1007/s11548-014-1128-3 · 1.66 Impact Factor
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    ABSTRACT: Purpose: To efficiently compute a physiologically realistic spatial transformation from a sparse point cloud of displacement estimates using Moving Least Squares (MLS) and any combination of upper bound, lower bound, or equality constraints placed on the Jacobian. Whereas diffeomorphic deformable image registration (DIR) requires the transformation's Jacobian determinant to be positive, within the context of thoracic CT, a more appropriate constraint is to require positive Jacobian values that reflect a strictly contracting volume (inhale to exhale DIR) or expanding volume (exhale to inhale DIR).
    Medical Physics 06/2014; 41(6):202-202. DOI:10.1118/1.4888250 · 3.01 Impact Factor
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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; 73(4). DOI:10.1002/mrm.25279 · 3.40 Impact Factor
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    ABSTRACT: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimall1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download atwww.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations.
    Medical Physics 04/2014; 41(4):041904. DOI:10.1118/1.4866891 · 3.01 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; DOI:10.3109/02656736.2013.864424 · 2.77 Impact Factor
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    ABSTRACT: Purpose: MR‐guided laser‐induced thermal therapy (MRgLITT) is an emerging minimally invasive neurosurgical tool being explored as a treatment alternative for conditions such as motion disorder, radiation necrosis, and intracranial metastases. The primary goal is to reduce complications and normal tissue morbidity associated with conventional surgery. Computational models are being investigated to aid prospective LITT planning; however, accuracy is undermined by imprecise and non‐patient specific knowledge of parameters. This work explores incorporating uncertainty quantification (UQ) of temperature output from the stochastic Pennes bioheat transfer equation (BHT). Methods: A five parameter (perfusion, thermal conductivity, optical absorption, optical scattering) stochastic BHT LITT model was used. Parameters were considered to be uniform distributions with ranges informed by literature values. Generalized polynomial chaos (gPC) was employed to calculate spatio‐temporal, voxel‐wise functions of the output temperature distributions for UQ. BHT parameter sensitivity in linear and nonlinear models was explored in silico using univariate gPC. Retrospective analysis of MR thermography (MRTI) from both phantom and MRgLITT in normal canine brain in vivo (n=4) was explored using multivariate gPC. Isotherms, temporal and linear profiles were reported. Results: Univariate simulations demonstrated that optical parameters explained the majority of model variance (peak standard deviation: anisotropy 3.75 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.42 °C, and perfusion 0.94 °C). Linear model variance enclosed nonlinear model variance. Mean temperature and 95% confidence interval from multivariate simulations correlated well with measured heating even near the applicator. Conclusion: gPC may provide robust and relatively fast UQ facilitating useful prospective LITT planning in brain tissue despite imprecise knowledge of parameters. The faster linear simulation approximated the nonlinear simulation without excessive variance. Further, the computational burden was reduced with minimal accuracy loss by including only the most sensitive parameters. Subsequent work includes applying stochastic BHT to retrospective human brain tumor LITT.
    Medical Physics 06/2013; 40(6):485. DOI:10.1118/1.4815572 · 3.01 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; DOI:10.3109/02656736.2013.798036 · 2.77 Impact Factor
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    ABSTRACT: Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts.
    Physics in Medicine and Biology 04/2013; 58(9):2861-2877. DOI:10.1088/0031-9155/58/9/2861 · 2.92 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; DOI:10.1007/s10439-012-0638-9 · 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. DOI:10.1118/1.4734888 · 3.01 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. DOI:10.1118/1.4735746 · 3.01 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.
    12/2011; 31(4):984-94. DOI:10.1109/TMI.2011.2181185
  • 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 12/2011; 27(8):751-61. DOI:10.3109/02656736.2011.611962 · 2.77 Impact Factor
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    ABSTRACT: CONCLUSION The use of automated image processing and analysis of multiple prior examinations is possible in a PACS environment using a cluster of minicomputers to accelerate data transfer . BACKGROUND The increasing number of radiologic examinations and the number of images per exam threatens to outpace Moore’s law which anticipates a doubling of computing power every 18 months. While supercomputers are available at large academic and commercial enterprises, the infrastructure needed to reliably and efficiently transfer and process petabytes of data is still an unsolved issue. We present an image processing cluster, of headless minicomputers dedicated to image processing and decision support, that could even be used by smaller radiology practices. EVALUATION Fifteen Zotac-mag minicomputers were used in developing the cluster. Each computer has a 160 gigabyte hard drive, 2 gigabytes RAM, 1-gigabit Ethernet connection, and no monitor or peripherals devices. The cluster is backed by an 11-terabyte network storage device. As a radiologist opens an imaging study for review using a standard PACS system, the cluster loads individual prior examinations on each drone computer. A custom software application provides for feature extraction from each of the studies based on automated image analysis . Post-processed data are then made available for remote visualization over the network for presentation on the local PACS workstation. DISCUSSION A proof of concept distributed image processing system was developed and tested using our testing PACS server environment. Use of multiple inexpensive minicomputers as servers with their own gigabit ethernet connections may allow for faster data transfer than having a single more powerful server with a single gigabit ethernet connection. Simultaneous maximum intensity projections (MIPS) of multiple prior CT examinations is prototyped. Algorithms to pre-fetch and cache anticipated imaging data are under further development to improve throughput. Future work in machine learning algorithms will be essential to assist the radiologist in assessing and gathering new information from the large amounts of imaging data collected from the multitude of exams.
    Radiological Society of North America 2011 Scientific Assembly and Annual Meeting; 11/2011
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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 08/2011; 27(5):453-64. DOI:10.3109/02656736.2011.557028 · 2.77 Impact Factor
  • Yusheng Feng, David 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; 28(3-28):134 - 138. DOI:10.1109/MSP.2011.940419 · 4.48 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. DOI:10.1016/j.jvir.2010.07.022 · 2.15 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; 57(5-57):1024 - 1030. DOI:10.1109/TBME.2009.2037733 · 2.23 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. DOI:10.1615/CritRevBiomedEng.v38.i1.70

Publication Stats

192 Citations
62.20 Total Impact Points

Institutions

  • 2010–2014
    • University of Texas MD Anderson Cancer Center
      • • Department of Imaging Physics
      • • Division of Radiation Oncology
      Houston, Texas, United States
  • 2011
    • University of Houston
      Houston, Texas, United States
  • 2006–2009
    • University of Texas at Austin
      • Institute for Computational Engineering and Sciences
      Austin, Texas, United States