[Show abstract][Hide abstract] ABSTRACT: Hyperpolarized 1-13C-pyruvate has shown tremendous promise as an agent for imaging tumor metabolism with unprecedented sensitivity and specificity. Imaging hyperpolarized substrates by magnetic resonance is unlike traditional MRI because signals are highly transient and their spatial distribution varies continuously over their observable lifetime. Therefore, new imaging approaches are needed to ensure optimal measurement under these circumstances. Constrained reconstruction algorithms can integrate prior information, including biophysical models of the substrate/target interaction, to reduce the amount of data that is required for image analysis and reconstruction. In this study, we show that metabolic MRI with hyperpolarized pyruvate is biased by tumor perfusion, and present a new pharmacokinetic model for hyperpolarized substrates that accounts for these effects. The suitability of this model is confirmed by statistical comparison to alternates using data from 55 dynamic spectroscopic measurements in normal animals and murine models of anaplastic thyroid cancer, glioblastoma, and triple-negative breast cancer. The kinetic model was then integrated into a constrained reconstruction algorithm and feasibility was tested using significantly under-sampled imaging data from tumor-bearing animals. Compared to naïve image reconstruction, this approach requires far fewer signal-depleting excitations and focuses analysis and reconstruction on new information that is uniquely available from hyperpolarized pyruvate and its metabolites, thus improving the reproducibility and accuracy of metabolic imaging measurements.
Cancer Research 11/2015; 75(22):4708-17. DOI:10.1158/0008-5472.CAN-15-0171 · 9.33 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: An accelerated model-based information theoretic approach is presented to
perform the task of Magnetic Resonance (MR) thermal image reconstruction from a
limited number of observed samples on k-space. The key idea of the proposed
approach is to utilize information theoretic techniques to optimally detect
samples of k-space that are information rich with respect to a model of the
thermal data acquisition. These highly informative k-space samples are then
used to refine the mathematical model and reconstruct the image. The
information theoretic reconstruction is demonstrated retrospectively in data
acquired during MR guided Laser Induced Thermal Therapy (MRgLITT) procedures.
The approach demonstrates that locations of high-information content with
respect to a model based reconstruction of MR thermometry may be quantitatively
identified. The predicted locations of high-information content are sorted and
retrospectively extracted from the fully sampled k-space measurements data set.
The effect of interactively increasing the predicted number of data points used
in the subsampled reconstruction is quantified using the L2-norm of the
distance between the subsampled and fully sampled reconstruction. Performance
of the proposed approach is also compared with clinically available subsampling
techniques (rectilinear subsampling and variable-density Poisson disk
undersampling). It is shown that the proposed subsampling scheme results in
accurate reconstructions using small fraction of k-space points and suggest
that the reconstruction technique may be useful in improving the efficiency of
the thermometry data temporal resolution.
[Show abstract][Hide abstract] ABSTRACT: A cross-validation analysis evaluating computer model prediction accuracy for a priori planning magnetic resonance-guided laser-induced thermal therapy (MRgLITT) procedures in treating focal diseased brain tissue is presented. Two mathematical models are considered. (1) A spectral element discretisation of the transient Pennes bioheat transfer equation is implemented to predict the laser-induced heating in perfused tissue. (2) A closed-form algorithm for predicting the steady-state heat transfer from a linear superposition of analytic point source heating functions is also considered. Prediction accuracy is retrospectively evaluated via leave-one-out cross-validation (LOOCV). Modelling predictions are quantitatively evaluated in terms of a Dice similarity coefficient (DSC) between the simulated thermal dose and thermal dose information contained within N = 22 MR thermometry datasets. During LOOCV analysis, the transient model’s DSC mean and median are 0.7323 and 0.8001 respectively, with 15 of 22 DSC values exceeding the success criterion of DSC ≥ 0.7. The steady-state model’s DSC mean and median are 0.6431 and 0.6770 respectively, with 10 of 22 passing. A one-sample, one-sided Wilcoxon signed-rank test indicates that the transient finite element method model achieves the prediction success criteria, DSC ≥ 0.7, at a statistically significant level.
International Journal of Hyperthermia 10/2015; DOI:10.3109/02656736.2015.1055831 · 2.65 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patient-specific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.
Archives of Computational Methods in Engineering 06/2015; DOI:10.1007/s11831-015-9156-x · 3.68 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose:
Nanoparticle Mediated Laser Interstitial Thermal Therapy (npLITT) is a technique that utilizes tumor localized optically activated nanoparticles to increase the conformality of laser ablation procedures. Temperatures in these procedures are dependent on the particle concentration which generally cannot be measured noninvasively prior to therapy. In this work we attempt to quantify particle concentration in vivo by estimating the increase in R2* relaxation induced by bifunctional magnetic resonance (MR)-visible gold-based nanoparticles (SPIO@Au) and relate it to the temperature increase observed during real time MR temperature imaging (MRTI) of laser ablation.
Medical Physics 06/2015; 42(6):3566. DOI:10.1118/1.4925413 · 2.64 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose:
MR-guided laser induced thermal therapy (MRgLITT) is a minimally invasive surgery with applications in the brain, among other sites. In especially precise interventions, like neurosurgery, accurate planning may behoove surgical planning by aiding in the decision of where and how many laser ablations are required. Previous models of tissue heating have relied on literature values extrapolated primarily from normal brain
animal research and ex vivo data. In this abstract, an inverse problem provides model parameter data from retrospective analysis of MR temperature imaging data in patient tumor tissue, which represent a training cohort. Within the same cohort, leave-one-out cross validation (LOOCV) estimates the predictive accuracy of the trained model.
Medical Physics 06/2015; 42(6):3196. DOI:10.1118/1.4923813 · 2.64 Impact Factor
[Show abstract][Hide abstract] 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 · 1.71 Impact Factor
[Show abstract][Hide abstract] 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 · 2.64 Impact Factor
[Show abstract][Hide abstract] 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 · 2.64 Impact Factor
[Show abstract][Hide abstract] 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; 30(1). DOI:10.3109/02656736.2013.864424 · 2.65 Impact Factor
[Show abstract][Hide abstract] 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 · 2.64 Impact Factor
[Show abstract][Hide abstract] 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; 29(4). DOI:10.3109/02656736.2013.798036 · 2.65 Impact Factor
[Show abstract][Hide abstract] 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 · 2.76 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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 · 2.64 Impact Factor
[Show abstract][Hide abstract] 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 · 2.64 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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 · 2.65 Impact Factor