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Gao T, Liang L, Ding H, Wang G. Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method. Phys Med Biol 2024; 69:105019. [PMID: 38648787 DOI: 10.1088/1361-6560/ad4194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predictingin vivobrain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy.Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT.Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations.Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction.Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.
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Affiliation(s)
- Tingting Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Libin Liang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Hui Ding
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Guangzhi Wang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
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2
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Predictive modeling of brain tumor laser ablation dynamics. J Neurooncol 2019; 144:193-203. [PMID: 31240526 DOI: 10.1007/s11060-019-03220-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 06/16/2019] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Laser interstitial thermal therapy (LITT) is a novel MR thermometry-guided thermoablative tool revolutionizing the clinical management of brain tumors. A limitation of LITT is our inability to estimate a priori how tissues will respond to thermal energy, which hinders treatment planning and delivery. The aim of this study was to determine whether brain tumor LITT ablation dynamics may be predicted by features of the preoperative MRI and the relevance of these data, if any, to the recurrence of metastases after LITT. METHODS Intraoperative thermal damage estimate (TDE) map pixels representative of irreversible damage were retrospectively quantified relative to ablation onset for 101 LITT procedures. Raw TDE pixel counts and TDE pixel counts modelled with first order dynamics were related to eleven independent variables derived from the preoperative MRI, demographics, laser settings, and tumor pathology. Stepwise regression analysis generated predictive models of LITT dynamics, and leave-one-out cross validation evaluated the accuracy of these models at predicting TDE pixel counts solely from the independent variables. Using a deformable atlas, TDE maps were co-registered to the immediate post-ablation MRI, allowing comparison of predicted and actual ablation sizes. RESULTS Brain tumor TDE pixel counts modelled with first order dynamics, but not raw pixel counts, are correlated with the independent variables. Independent variables showing strong relations to the TDE pixel measures include T1 gadolinium and T2 signal, perfusion, and laser power. Associations with tissue histopathology are minimal. Leave-one-out analysis demonstrates that predictive models using these independent variables account for 77% of the variance observed in TDE pixel counts. Analysis of metastases treated revealed a trend towards the over-estimation of LITT effects by TDE maps during rapid ablations, which was associated with tumor recurrence. CONCLUSIONS Features of the preoperative MRI are predictive of LITT ablation dynamics and could eventually be used to improve the clinical efficacy with which LITT is delivered to brain tumors.
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Naser MA, Sampaio DRT, Muñoz NM, Wood CA, Mitcham TM, Stefan W, Sokolov KV, Pavan TZ, Avritscher R, Bouchard RR. Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:561-571. [PMID: 30207951 PMCID: PMC6445252 DOI: 10.1109/tmi.2018.2867602] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO2) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO2) and in vivo rat arteries. Estimation error of absolute SO2 and change in SO2 reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO2 with PA imaging.
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Affiliation(s)
- Mohamed A. Naser
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Diego R. T. Sampaio
- Department of Physics, University of Sao Paulo, Ribeirao Preto, SP 14040-901, BRAZIL
| | - Nina M. Muñoz
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Cayla A. Wood
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
| | - Trevor M. Mitcham
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
| | - Wolfgang Stefan
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Konstantin V. Sokolov
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
| | - Theo Z. Pavan
- Department of Physics, University of Sao Paulo, Ribeirao Preto, SP 14040-901, BRAZIL
| | - Rony Avritscher
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Richard R. Bouchard
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA ().; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
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Silva D, Sharma M, Barnett GH. Laser Ablation vs Open Resection for Deep-Seated Tumors: Evidence for Laser Ablation. Neurosurgery 2018; 63 Suppl 1:15-26. [PMID: 27399359 DOI: 10.1227/neu.0000000000001289] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Danilo Silva
- Department of Neurosurgery, Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio
| | - Mayur Sharma
- Department of Neurosurgery, Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio
| | - Gene H Barnett
- Department of Neurosurgery, Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio
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Mauri G, Nicosia L, Della Vigna P, Varano GM, Maiettini D, Bonomo G, Giuliano G, Orsi F, Solbiati L, De Fiori E, Papini E, Pacella CM, Sconfienza LM. Percutaneous laser ablation for benign and malignant thyroid diseases. Ultrasonography 2018; 38:25-36. [PMID: 30440161 PMCID: PMC6323312 DOI: 10.14366/usg.18034] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 09/17/2018] [Indexed: 02/06/2023] Open
Abstract
Minimally invasive image-guided thermal ablation is becoming increasingly common as an alternative to surgery for the treatment of benign thyroid nodules. Among the various techniques for thermal ablation, laser ablation (LA) is the least invasive, using the smallest applicators available on the market and enabling extremely precise energy deposition. However, in some cases, multiple laser fibers must be used simultaneously for the treatment of large nodules. In this review, the LA technique is described, and its main clinical applications and results are discussed and illustrated.
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Affiliation(s)
- Giovanni Mauri
- Department of Interventional Radiology, European Institute of Oncology, Milan, Italy
| | - Luca Nicosia
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Paolo Della Vigna
- Department of Interventional Radiology, European Institute of Oncology, Milan, Italy
| | - Gianluca Maria Varano
- Department of Interventional Radiology, European Institute of Oncology, Milan, Italy
| | - Daniele Maiettini
- Department of Interventional Radiology, European Institute of Oncology, Milan, Italy
| | - Guido Bonomo
- Department of Interventional Radiology, European Institute of Oncology, Milan, Italy
| | | | - Franco Orsi
- Department of Interventional Radiology, European Institute of Oncology, Milan, Italy
| | - Luigi Solbiati
- Department of Radiology, Humanitas University, IRCCS Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Elvio De Fiori
- Department of Radiology, European Institute of Oncology, Milan, Italy
| | - Enrico Papini
- Endocrinology Department, Regina Apostolorum Hospital, Albano Laziale, Italy
| | - Claudio Maurizio Pacella
- Department of Diagnostic Imaging and Interventional Radiology, Regina Apostolorum Hospital, Rome, Italy
| | - Luca Maria Sconfienza
- Unit of Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Department of Biomedical Sciences for Health, University of Milano, Milan, Italy
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Silva D, Sharma M, Juthani R, Meola A, Barnett GH. Magnetic Resonance Thermometry and Laser Interstitial Thermal Therapy for Brain Tumors. Neurosurg Clin N Am 2017; 28:525-533. [PMID: 28917281 DOI: 10.1016/j.nec.2017.05.015] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Recent technological advancements in intraoperative imaging are shaping the way for a new era in brain tumor surgery. Magnetic resonance thermometry has provided intraoperative real-time imaging feedback for safe and effective application of laser interstitial thermal therapy (LITT) in neuro-oncology. Thermal ablation has also established itself as a surgical option in epilepsy surgery and is currently used in spine oncology with promising results. This article reviews the principles and rationale as well as the clinical application of LITT for brain tumors. It also discusses the technical nuances of the current commercially available systems.
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Affiliation(s)
- Danilo Silva
- Rose Ella Burkhardt Brain Tumor and Neuro-oncology Center, Department of Neurosurgery, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
| | - Mayur Sharma
- Rose Ella Burkhardt Brain Tumor and Neuro-oncology Center, Department of Neurosurgery, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
| | - Rupa Juthani
- Rose Ella Burkhardt Brain Tumor and Neuro-oncology Center, Department of Neurosurgery, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
| | - Antonio Meola
- Rose Ella Burkhardt Brain Tumor and Neuro-oncology Center, Department of Neurosurgery, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
| | - Gene H Barnett
- Rose Ella Burkhardt Brain Tumor and Neuro-oncology Center, Department of Neurosurgery, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
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Goharrizi AY, Kwong RH, Chopra R. A self-tuning adaptive controller for 3-D image-guided ultrasound cancer therapy. IEEE Trans Biomed Eng 2014; 61:911-9. [PMID: 24557692 DOI: 10.1109/tbme.2013.2292559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
One of the challenges in MRI-controlled hyperthermia cancer treatment for localized tumor is that the tissue properties are dynamic and difficult to measure. Therefore, tuning the optimal gains for a constant gain controller can be challenging. In this paper, a new multi-input single-output adaptive controller strategy is proposed to address these problems. The inputs to the controller block are the frequency, rotation rate, and applied power level of an interstitial applicator, and the output is the boundary temperature during treatment. The time-varying gains of the new controller are updated over time using Lyapunov-based stability analysis. The robustness of the new controller to changes in the parameters of the tissue is investigated and compared to a constant gain controller through simulation studies. Simulations take into account changes in tissue properties and other conditions that may be encountered in a practical clinical situation. Finally, the effectiveness of the proposed controller is validated through an experimental study.
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Cepek J, Lindner U, Davidson SRH, Haider MA, Ghai S, Trachtenberg J, Fenster A. Treatment planning for prostate focal laser ablation in the face of needle placement uncertainty. Med Phys 2013; 41:013301. [DOI: 10.1118/1.4842535] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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Yeniaras E, Fuentes DT, Fahrenholtz SJ, Weinberg JS, Maier F, Hazle JD, Stafford RJ. Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain. Int J Comput Assist Radiol Surg 2013; 9:659-67. [PMID: 24091853 DOI: 10.1007/s11548-013-0948-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2013] [Accepted: 09/14/2013] [Indexed: 11/28/2022]
Abstract
PURPOSE An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility. METHODS A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes' bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images. RESULTS The total time to initialize and simulate an MRgLITT procedure using the GUI was [Formula: see text]5 min. Each independent simulation took [Formula: see text]30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour [Formula: see text] was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm [Formula: see text], whereas the mean Dice similarity coefficient was 0.93 [Formula: see text]. CONCLUSIONS We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.
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Affiliation(s)
- E Yeniaras
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA,
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10
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Fahrenholtz SJ, Stafford RJ, Maier F, Hazle JD, Fuentes D. Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures. Int J Hyperthermia 2013; 29:324-35. [PMID: 23692295 DOI: 10.3109/02656736.2013.798036] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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.
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Affiliation(s)
- Samuel J Fahrenholtz
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77054, USA
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11
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Fuentes D, Elliott A, Weinberg JS, Shetty A, Hazle JD, Stafford RJ. An inverse problem approach to recovery of in vivo nanoparticle concentrations from thermal image monitoring of MR-guided laser induced thermal therapy. Ann Biomed Eng 2012; 41:100-11. [PMID: 22918665 DOI: 10.1007/s10439-012-0638-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Accepted: 08/04/2012] [Indexed: 12/15/2022]
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.
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Affiliation(s)
- D Fuentes
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
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12
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Melancon MP, Stafford RJ, Li C. Challenges to effective cancer nanotheranostics. J Control Release 2012; 164:177-82. [PMID: 22906841 DOI: 10.1016/j.jconrel.2012.07.045] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Revised: 07/09/2012] [Accepted: 07/14/2012] [Indexed: 01/15/2023]
Abstract
Advances in nanotechnology for oncology will arise from an increased understanding of the interaction between nanomaterials and biological systems; refinement of multifunctional nanocomposites for applications such as simultaneous imaging and therapy (theranostics); and harnessing of the unique physicochemical properties arising from nanoscale effects which distinguish them from small-molecular-weight molecules in the detection and destruction of cancer cells with high selectivity and efficiency. The major challenges in successful clinical translation of tumor specific nanoparticle delivery include overcoming various biological barriers and demonstrating enhanced therapeutic efficacy over the current standard of care in the clinic. For many nanoparticle mediated theranostic applications, image guidance can play a crucial role not only in exploiting the cancer specific imaging capabilities of these novel particles, but in planning, targeting, monitoring and verifying treatment delivery, thus enhancing the safety and efficacy of these emerging procedures.
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Affiliation(s)
- Marites P Melancon
- Department of Experimental Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Fuentes D, Yung J, Hazle JD, Weinberg JS, Stafford RJ. Kalman filtered MR temperature imaging for laser induced thermal therapies. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:984-94. [PMID: 22203706 PMCID: PMC3873725 DOI: 10.1109/tmi.2011.2181185] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
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.
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Affiliation(s)
- D. Fuentes
- The University of Texas M.D Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA
| | - J. Yung
- The University of Texas M.D Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA
| | - J. D. Hazle
- The University of Texas M.D Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA
| | - J. S. Weinberg
- The University of Texas M.D Anderson Cancer Center, Department of Neurosurgery, Houston TX 77030, USA
| | - R. J. Stafford
- The University of Texas M.D Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA
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Fuentes D, Cardan R, Stafford RJ, Yung J, Dodd GD, Feng Y. High-fidelity computer models for prospective treatment planning of radiofrequency ablation with in vitro experimental correlation. J Vasc Interv Radiol 2011; 21:1725-32. [PMID: 20920840 DOI: 10.1016/j.jvir.2010.07.022] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2009] [Revised: 06/18/2010] [Accepted: 07/15/2010] [Indexed: 12/20/2022] Open
Abstract
PURPOSE 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. MATERIALS AND METHODS 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- David Fuentes
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
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FUENTES D, WALKER C, ELLIOTT A, SHETTY A, HAZLE J, STAFFORD R. Magnetic resonance temperature imaging validation of a bioheat transfer model for laser-induced thermal therapy. Int J Hyperthermia 2011; 27:453-64. [PMID: 21756043 PMCID: PMC3930085 DOI: 10.3109/02656736.2011.557028] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- D. FUENTES
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston
| | - C. WALKER
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston
| | - A. ELLIOTT
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston
| | | | - J.D. HAZLE
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston
| | - R.J. STAFFORD
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston
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Feng Y, Fuentes D. Model-based planning and real-time predictive control for laser-induced thermal therapy. Int J Hyperthermia 2011; 27:751-61. [PMID: 22098360 PMCID: PMC3930104 DOI: 10.3109/02656736.2011.611962] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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.
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Affiliation(s)
- Yusheng Feng
- Computational Bioengineering and Control Lab, The University of Texas at San Antonio, USA.
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