Bio-heat transfer model of electroconvulsive therapy: Effect of biological properties on induced temperature variation

Oliveira, Marília Menezes de; Wen, Peng; Ahfock, Tony

doi:10.1109/embc.2016.7591603

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…The values for the electric parameters of the various tissues were taken from Miranda et al (2014), which were chosen based on a literature review. The values for the thermal properties were also carefully chosen after an extensive literature review (van Leeuwen et al 2000, Ley and Bayazitoglu 2003, Collins et al 2004, Connor and Hynynen 2004, Ley and Bayazitoglu 2004, Janssen et al 2005a, 2005b, Elwassif et al 2006, Kim et al 2006, Sukstanskii and Yablonskiy 2006, Pinton et al 2010, Tullius and Bayazitoglu 2013, Oliveira et al 2016, Fitzgerald et al 2018. All values are listed in table 1.…”

Section: Solving the Modelmentioning

confidence: 99%

Temperature control in TTFields therapy of GBM: impact on the duty cycle and tissue temperature

Gentilal

Salvador²,

Miranda

2019

Phys. Med. Biol.

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In TTFields therapy, Optune® is used to deliver the electric field to the tumor via 4 transducer arrays. This device monitors the temperature of the transducers and reduces the current whenever a transducer reaches 41 °C. Our aim is to quantify Optune’s duty cycle and to predict the steady-state temperature distribution in the head during GBM treatment. We used a realistic head model and the finite element method to solve Pennes equation and to simulate how Optune operates considering that current reduces to zero when the thermal limit is reached. The thermal impact was evaluated considering the maximum temperature reached by each tissue and using the CEM 43 °C metric. We observed that Optune switches the current on and off intermittently. In our model, one transducer reached the temperature limit quicker than the others and consequently it was the one that controlled current injection. This led to different duty cycles for the anterior–posterior and left–right array pairs. The thermal analysis indicated that the highest temperature in the model, 41.7 °C, was reached on the scalp under a transducer. However, TTFields may lead to significant changes only at the brain level such as BBB permeability increase, cerebral blood flow variation and changes in the concentration of some neurotransmitters. The duty cycle may be increased, e.g. by controlling the current at the transducer level. These predictions should be validated by comparison with experimental data and reconciled with the lack of evidence of thermal impact in clinical trials.

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Section: Solving the Modelmentioning

confidence: 99%

Temperature control in TTFields therapy of GBM: impact on the duty cycle and tissue temperature

Gentilal

Salvador²,

Miranda

2019

Phys. Med. Biol.

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“…ELECTRICAL properties (EP), namely electric permittivity and conductivity, dictate the interaction between electromagnetic (EM) waves and materials [1], [2]. Accurate estimation of the EP of biological tissue could improve therapeutic applications, such as radiofrequency (RF) ablation [3], [4], tailored transcranial magnetic stimulation [5], [6], and hyperthermia treatment [7], [8], [4], among others. Magnetic resonance (MR) is a promising tool for robust EP estimation, since it can provide tomographic measurements that reflect the curvature of the EM field inside an object due to EP.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Estimation of Electrical Properties From Magnetic Resonance Measurements via Global Maxwell Tomography and Match Regularization

Serralles

Lattanzi

Giannakopoulos

et al. 2020

IEEE Trans. Biomed. Eng.

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Objective: In this paper, we introduce Global Maxwell Tomography (GMT), a novel, volumetric technique that estimates electric conductivity and permittivity by solving an inverse scattering problem based on magnetic resonance measurements. Methods: GMT relies on a fast volume integral equation solver, MARIE, for the forward path and a novel regularization method, Match Regularization, designed specifically for electrical properties estimation from noisy measurements. We performed simulations with three different tissue-mimicking numerical phantoms of different complexity, using synthetic transmit sensitivity maps with realistic noise levels as the measurements. We performed an experiment at 7T using an 8-channel coil and a uniform phantom. Results: We showed that GMT could estimate relative permittivity and conductivity from noisy magnetic resonance measurements with an average error as low as 0.3% and 0.2%, respectively, over the entire volume of the numerical phantom. Voxel resolution did not affect GMT performance and is currently limited only by the memory of the Graphics Processing Unit. In the experiment, GMT could estimate electrical properties within 5% of the values measured with a dielectric probe. Conclusion: This work demonstrated the feasibility of GMT with Match Regularization, suggesting that it could be effective for accurate in vivo electrical property estimation. GMT does not rely on any symmetry assumption for the electromagnetic field and can be generalized to estimate also the spin magnetization, at the expenses of increased computational complexity. Significance: GMT could provide insight into the distribution of electromagnetic fields inside the body, which represents one of the key ongoing challenges for various diagnostic and therapeutic applications.

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“…Studies in the past two decades have shown that various diseases lead to local changes in EP relative to healthy tissue, such as cerebral ischemia [6], [7], glioma [8], [9], pelvic tumours [10], and breast cancer [11]. Therefore, accurate estimation of tissue EPs can be helpful for disease diagnosis, treatment monitoring, and improving the effectiveness of existing therapeutic modalities such as radiofrequency hyperthermia [12]- [14]. Moreover, access to EP can provide insight into the electromagnetic field distribution in tissue, which could facilitate patient-specific studies in magnetic resonance (MR) imaging (MRI).…”

mentioning

confidence: 99%

PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks

Yu¹,

Serralles²,

Giannakopoulos³

et al. 2023

Preprint

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In this paper, we introduce Physics-Informed Fourier Networks (PIFONs) for Electrical Properties (EP) Tomography (EPT). Our novel deep learning-based method is capable of learning EPs globally by solving an inverse scattering problem based on noisy and/or incomplete magnetic resonance (MR) measurements. Methods: We use two separate fully-connected neural networks, namely B + 1 Net and EP Net, to learn the B + 1 field and EPs at any location. A random Fourier features mapping is embedded into B + 1 Net, which allows it to learn the B + 1 field more efficiently. These two neural networks are trained jointly by minimizing the combination of a physicsinformed loss and a data mismatch loss via gradient descent. Results: We showed that PIFON-EPT could provide physically consistent reconstructions of EPs and transmit field in the whole domain of interest even when half of the noisy MR measurements of the entire volume was missing. The average error was 2.49%, 4.09% and 0.32% for the relative permittivity, conductivity and B + 1 , respectively, over the entire volume of the phantom. In experiments that admitted a zero assumption of Bz, PIFON-EPT could yield accurate EP predictions near the interface between regions of different EP values without requiring any boundary conditions. Conclusion: This work demonstrated the feasibility of PIFON-EPT, suggesting it could be an accurate and effective method for electrical properties estimation. Significance: PIFON-EPT can efficiently de-noise MR measurements, which shows the potential to improve other MR-based EPT techniques. Furthermore, it is the first time that MR-based EPT methods can reconstruct the EPs and B + 1 field simultaneously from incomplete simulated noisy MR measurements.Index terms-Electrical Property Mapping, Physics Informed Neural Networks, Fourier Features Mapping. I. INTRODUCTIONElectrical properties (EP), namely relative permittivity and electric conductivity, determine the interactions between electromagnetic waves and biological tissue [1], [2]. EPs have the potential to be employed as biomarkers for pathologies

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Bio-heat transfer model of electroconvulsive therapy: Effect of biological properties on induced temperature variation

Cited by 5 publications

References 16 publications

Temperature control in TTFields therapy of GBM: impact on the duty cycle and tissue temperature

Temperature control in TTFields therapy of GBM: impact on the duty cycle and tissue temperature

Noninvasive Estimation of Electrical Properties From Magnetic Resonance Measurements via Global Maxwell Tomography and Match Regularization

PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks

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