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Liu C, Wang Q, Si W, Ni X. NuTracker: a coordinate-based neural network representation of lung motion for intrafraction tumor tracking with various surrogates in radiotherapy. Phys Med Biol 2022; 68. [PMID: 36537890 DOI: 10.1088/1361-6560/aca873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 12/01/2022] [Indexed: 12/03/2022]
Abstract
Objective. Tracking tumors and surrounding tissues in real-time is critical for reducing errors and uncertainties during radiotherapy. Existing methods are either limited by the linear representation or scale poorly with the volume resolution. To address both issues, we propose a novel coordinate-based neural network representation of lung motion to predict the instantaneous 3D volume at arbitrary spatial resolution from various surrogates: patient surface, fiducial marker, and single kV projection.Approach. The proposed model, namely NuTracker, decomposes the 4DCT into a template volume and dense displacement fields (DDFs), and uses two coordinate neural networks to predict them from spatial coordinates and surrogate states. The predicted template is spatially warped with the predicted DDF to produce the deformed volume for a given surrogate state. The nonlinear coordinate networks enable representing complex motion at infinite resolution. The decomposition allows imposing different regularizations on the spatial and temporal domains. The meta-learning and multi-task learning are used to train NuTracker across patients and tasks, so that commonalities and differences can be exploited. NuTracker was evaluated on seven patients implanted with markers using a leave-one-phase-out procedure.Main results. The 3D marker localization error is 0.66 mm on average and <1 mm at 95th-percentile, which is about 26% and 32% improvement over the predominant linear methods. The tumor coverage and image quality are improved by 5.7% and 11% in terms of dice and PSNR. The difference in the localization error for different surrogates is small and is not statistically significant. Cross-population learning and multi-task learning contribute to performance. The model tolerates surrogate drift to a certain extent.Significance. NuTracker can provide accurate estimation for entire tumor volume based on various surrogates at infinite resolution. It is of great potential to apply the coordinate network to other imaging modalities, e.g. 4DCBCT and other tasks, e.g. 4D dose calculation.
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Affiliation(s)
- Cong Liu
- Radiation Oncology Center, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, People's Republic of China.,Center of Medical Physics, Nanjing Medical University, Changzhou, People's Republic of China.,Faculty of Business Information, Shanghai Business School, Shanghai, People's Republic of China
| | - Qingxin Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China
| | - Wen Si
- Faculty of Business Information, Shanghai Business School, Shanghai, People's Republic of China.,Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Xinye Ni
- Radiation Oncology Center, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, People's Republic of China.,Center of Medical Physics, Nanjing Medical University, Changzhou, People's Republic of China
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Lin A, De Backer J, Quatannens D, Cuypers B, Verswyvel H, De La Hoz EC, Ribbens B, Siozopoulou V, Van Audenaerde J, Marcq E, Lardon F, Laukens K, Vanlanduit S, Smits E, Bogaerts A. The effect of local non-thermal plasma therapy on the cancer-immunity cycle in a melanoma mouse model. Bioeng Transl Med 2022; 7:e10314. [PMID: 36176603 PMCID: PMC9472020 DOI: 10.1002/btm2.10314] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/14/2022] [Accepted: 03/20/2022] [Indexed: 12/11/2022] Open
Abstract
Melanoma remains a deadly cancer despite significant advances in immune checkpoint blockade and targeted therapies. The incidence of melanoma is also growing worldwide, which highlights the need for novel treatment options and strategic combination of therapies. Here, we investigate non-thermal plasma (NTP), an ionized gas, as a promising, therapeutic option. In a melanoma mouse model, direct treatment of tumors with NTP results in reduced tumor burden and prolonged survival. Physical characterization of NTP treatment in situ reveals the deposited NTP energy and temperature associated with therapy response, and whole transcriptome analysis of the tumor identified several modulated pathways. NTP treatment also enhances the cancer-immunity cycle, as immune cells in both the tumor and tumor-draining lymph nodes appear more stimulated to perform their anti-cancer functions. Thus, our data suggest that local NTP therapy stimulates systemic, anti-cancer immunity. We discuss, in detail, how these fundamental insights will help direct the translation of NTP technology into the clinic and inform rational combination strategies to address the challenges in melanoma therapy.
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Affiliation(s)
- Abraham Lin
- PLASMANT‐Research GroupUniversity of AntwerpAntwerpen‐WilrijkBelgium
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | - Joey De Backer
- Department of Biomedical SciencesUniversity of AntwerpAntwerpen‐WilrijkBelgium
| | - Delphine Quatannens
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | - Bart Cuypers
- Adrem Data Lab, Department of Computer ScienceUniversity of AntwerpAntwerpenBelgium
| | - Hanne Verswyvel
- PLASMANT‐Research GroupUniversity of AntwerpAntwerpen‐WilrijkBelgium
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | | | - Bart Ribbens
- Industrial Vision Lab (InViLab)University of AntwerpAntwerpenBelgium
| | | | - Jonas Van Audenaerde
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | - Elly Marcq
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | - Filip Lardon
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer ScienceUniversity of AntwerpAntwerpenBelgium
| | - Steve Vanlanduit
- Industrial Vision Lab (InViLab)University of AntwerpAntwerpenBelgium
| | - Evelien Smits
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON)University of AntwerpAntwerpen‐WilrijkBelgium
| | - Annemie Bogaerts
- PLASMANT‐Research GroupUniversity of AntwerpAntwerpen‐WilrijkBelgium
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Wang G, Song X, Li G, Duan L, Li Z, Dai G, Bai L, Xiao Q, Zhang X, Song Y, Bai S. Correlation of Optical Surface Respiratory Motion Signal and Internal Lung and Liver Tumor Motion: A Retrospective Single-Center Observational Study. Technol Cancer Res Treat 2022; 21:15330338221112280. [PMID: 35791642 PMCID: PMC9272160 DOI: 10.1177/15330338221112280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Purpose: Surface-guided radiation therapy (SGRT) application has limitations. This study aimed to explore the relationship between patient characteristics and their external/internal correlation to qualitatively assess the external/internal correlation in a particular patient. Methods: Liver and lung cancer patients treated with radiotherapy in our institution were retrospectively analyzed. The external/internal correlation were calculated with Spearman correlation coefficient (SCC) and SCC after support vector regression (SVR) fitting (SCCsvr). The relationship between the external/internal correlation and magnitudes of motion of the tumor and external marker (Ai, Ae), tumor volume Vt, patient age, gender, and tumor location were explored. Results: The external/internal motions of liver and lung cancer patients were strongly correlated in the S-I direction, with mean SCCsvr values of 0.913 and 0.813. The correlation coefficients between the external/internal correlations and the patients’ characteristics (Ai, Ae, Vt, and age) were all smaller than 0.5; Ai, Ae and liver tumor volumes were positively correlated with the strength of the external/internal correlation, while lung tumor volumes and patient age were negative. The external/internal correlations in males and females were roughly equal, and the external/internal correlations in patients with peripheral lung cancers were stronger than those in patients with central lung cancers. Conclusion: The external/internal correlation shows great individual differences. The effects of Ai, Ae, Vt, and age are weakly to moderately correlated. Our results suggest the necessity of individualized assessment of patient's external/internal motion correlation prior to the application of SGRT technique for breath motion monitoring.
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Affiliation(s)
- Guangyu Wang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Xinyu Song
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Lian Duan
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Zhibin Li
- Department of Radiation Oncology, 74566The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guyu Dai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Long Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Xiangbin Zhang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Ying Song
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, 12530Sichuan University, Chengdu, China
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Zhang J, Xia W, Jin Q, Gao X. A 2D/3D Non-rigid Registration Method for Lung Images Based on a Non-linear Correlation Between Displacement Vectors and Similarity Measures. J Med Biol Eng 2021. [DOI: 10.1007/s40846-021-00609-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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5
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Zhang Y, Kashani R, Cao Y, Lawrence TS, Johansson A, Balter JM. A hierarchical model of abdominal configuration changes extracted from golden angle radial magnetic resonance imaging. Phys Med Biol 2021; 66:045018. [PMID: 33361579 PMCID: PMC7993537 DOI: 10.1088/1361-6560/abd66e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Abdominal organs are subject to a variety of physiological forces that superimpose their effects to influence local motion and configuration. These forces not only include breathing, but can also arise from cyclic antral contractions and a range of slow configuration changes. To elucidate each individual motion pattern as well as their combined effects, a hierarchical motion model was built for characterization of these 3 motion modes (characterized as deformation maps between states) using golden angle radial MR signals. Breathing motions are characterized first. Antral contraction states are then reconstructed after breathing motion-induced deformation are corrected; slow configuration change states are further extracted from breathing motion-corrected image reconstructions. The hierarchical model is established based on these multimodal states, which can be either individually shown or combined to demonstrate any arbitrary composited motion patterns. The model was evaluated using 20 MR scans acquired from 9 subjects. Poor reproducibility of breathing motions both within as well as between scan sessions was observed, with an average intra-subject difference of 1.6 cycles min-1 for average breathing frequencies of 12.0 cycles min-1. Antral contraction frequency distributions were more stable than breathing, but also presented poor reproducibility between scans with an average difference of 0.3 cycles min-1 for average frequencies of 3.2 cycles min-1. The magnitudes of motions beyond breathing were found to be significant, with 14.4 and 33.8 mm maximal motions measured from antral contraction and slow configuration changes, respectively. Hierarchical motion models have potential in multiple applications in radiotherapy, including improving the accuracy of dose delivery estimation, providing guidance for margin creation, and supporting advanced decisions and strategies for immobilization, treatment monitoring and gating.
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Affiliation(s)
- Yuhang Zhang
- Department of Radiation Oncology, University of Michigan, United States of America
- Department of Biomedical Engineering, University of Michigan, United States of America
| | - Rojano Kashani
- Department of Radiation Oncology, University of Michigan, United States of America
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, United States of America
- Department of Biomedical Engineering, University of Michigan, United States of America
- Department of Radiology, University of Michigan, United States of America
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, United States of America
| | - Adam Johansson
- Department of Surgical Sciences, Uppsala University, Sweden
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, United States of America
- Department of Biomedical Engineering, University of Michigan, United States of America
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Wang G, Li Z, Li G, Dai G, Xiao Q, Bai L, He Y, Liu Y, Bai S. Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy. Radiat Oncol 2021; 16:13. [PMID: 33446245 PMCID: PMC7807524 DOI: 10.1186/s13014-020-01729-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/06/2020] [Indexed: 02/08/2023] Open
Abstract
Background Surface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context. Methods Seven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, Dv. Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model. Results The models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update. Conclusions The LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.
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Affiliation(s)
- Guangyu Wang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Zhibin Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
| | - Guyu Dai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Long Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yisong He
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yaxin Liu
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.,College of Physics, Sichuan University, Chengdu, 610065, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting. LECTURE NOTES IN COMPUTER SCIENCE 2019. [DOI: 10.1007/978-3-030-20351-1_20] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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8
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Abayazid M, Kato T, Silverman SG, Hata N. Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions. Int J Comput Assist Radiol Surg 2018; 13:125-133. [PMID: 28766177 PMCID: PMC5754381 DOI: 10.1007/s11548-017-1644-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 07/10/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. MATERIALS AND METHODS The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. RESULTS The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. CONCLUSION The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.
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Affiliation(s)
- Momen Abayazid
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.
- MIRA-Institute for Biomedical Technology and Technical Medicine (Robotics and Mechatronics), University of Twente, Enschede, The Netherlands.
| | - Takahisa Kato
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
- Healthcare Optics Research Laboratory, Canon U.S.A., Inc., Cambridge, MA, USA
| | - Stuart G Silverman
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
| | - Nobuhiko Hata
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
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Wilms M, Werner R, Yamamoto T, Handels H, Ehrhardt J. Subpopulation-based correspondence modelling for improved respiratory motion estimation in the presence of inter-fraction motion variations. ACTA ACUST UNITED AC 2017; 62:5823-5839. [DOI: 10.1088/1361-6560/aa70cc] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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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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Estimating Internal Respiratory Motion from Respiratory Surrogate Signals Using Correspondence Models. 4D MODELING AND ESTIMATION OF RESPIRATORY MOTION FOR RADIATION THERAPY 2013. [DOI: 10.1007/978-3-642-36441-9_9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
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12
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Respiratory motion models: A review. Med Image Anal 2013; 17:19-42. [DOI: 10.1016/j.media.2012.09.005] [Citation(s) in RCA: 271] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 08/15/2012] [Accepted: 09/17/2012] [Indexed: 12/25/2022]
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Szegedi M, Rassiah-Szegedi P, Sarkar V, Hinkle J, Wang B, Huang YH, Zhao H, Joshi S, Salter BJ. Tissue characterization using a phantom to validate four-dimensional tissue deformation. Med Phys 2012; 39:6065-70. [DOI: 10.1118/1.4747528] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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