A conservative numerical scheme for modeling nonlinear acoustic propagation in thermoviscous homogeneous media

Diaz, Manuel A.; Solovchuk, Maxim; Sheu, Tony W. H.

doi:10.1016/j.jcp.2018.02.005

Cited by 18 publications

(5 citation statements)

References 26 publications

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“…The amplitude of this signal is set at 3.8 nm. According to Diaz et al [50], when the input pressure remains below p 0 = 0.35 MPa, the solutions obtained for a similar HIFU setup are within the linear regime. However, as the input pressure increases, the results enter a nonlinear regime and progressively deviate from the linear solution, particularly in the focal region.…”

Section: Simulation Proceduresmentioning

confidence: 82%

A Holistic Physics-Informed Neural Network Solution for Precise Destruction of Breast Tumors using High-Intensity Focused Ultrasound on a Realistic Breast Model

Lari,

Rajabzadeh,

Kohandel

et al. 2024

Preprint

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This study presents a novel approach for the precise ablation of breast tumors using high-intensity focused ultrasound (HIFU), leveraging a physics-informed neural network (PINN) integrated with a realistic breast model. The potential of high-intensity focused ultrasound (HIFU) in eliminating breast tumors has shown significant promise. This technique employs concentrated ultrasonic waves to generate intense heat, effectively destroying cancerous tissue. In previous finite element method (FEM) models, the computational demands of handling extensive datasets, multiple dimensions, and discretization pose significant challenges. Our PINN-based solution operates efficiently in a mesh-free domain, achieving remarkable accuracy with significantly reduced computational demands, compared to conventional FEM techniques. Additionally, employing PINN for estimating partial differential equations (PDE) solutions can notably decrease the enormous number of discretized elements needed. The model employs a bowlshaped acoustic transducer to focus ultrasound waves accurately on the tumor location. The simulation results offer detailed insights into each step of the HIFU process. By applying a 3.8 nm displacement amplitude of transducer input pulse at a frequency of 1.1 MHz for 1 second at the focal point, the focal point temperature reaches 38.4 °C. Subsequently, continuing the ablation process for another 90 seconds without the acoustic source leads to extensive necrosis of the tumor tissues. Validation of the PINN model’s accuracy was conducted through FEM analysis and a series of tests using ex vivo bovine liver, aligning closely with real-world HIFU therapy scenarios. This innovative platform provides physicians with a predictive tool to estimate the necrosis of tumor tissue, facilitating the customization of HIFU treatment strategies for individual breast cancer patients.

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Section: Simulation Proceduresmentioning

confidence: 82%

A Holistic Physics-Informed Neural Network Solution for Precise Destruction of Breast Tumors using High-Intensity Focused Ultrasound on a Realistic Breast Model

Lari,

Rajabzadeh,

Kohandel

et al. 2024

Preprint

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show abstract

“…At high intensities, the effects of nonlinear wave propagation become important and lead to a distortion of the waveform. Higher harmonics are generated due to the nonlinear distortion [39] , and a shock wave can form [50] , [51] . These nonlinear harmonics may affect the values of the predicted threshold pressures.…”

Section: Discussionmentioning

confidence: 99%

GPU-accelerated study of the inertial cavitation threshold in viscoelastic soft tissue using a dual-frequency driving signal

Filonets

Solovchuk

2022

Ultrasonics Sonochemistry

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“…For example, high-order time derivative terms hinder the use of a finite-difference time-domain (FDTD) scheme or a Fourier-type spectral scheme to solve Eq. ( 1) [53]. To avoid computational complexity associated with nonlinearity, the linearized Westervelt-Lighthill equation is used as…”

Section: A Westervelt-lighthill Equationmentioning

confidence: 99%

Multivariable-Incorporating Super-Resolution Convolutional Neural Network for Transcranial Focused Ultrasound Simulation

Shin¹,

Peng²,

Kim³

et al. 2023

Preprint

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<p>Transcranial focused ultrasound (tFUS) has emerged as a new non-invasive brain stimulation (NIBS) modality, with its exquisite ability to reach deep brain areas at a high spatial resolution. Accurate placement of an acoustic focus to a target region of the brain is crucial during tFUS treatment; however, distortion of acoustic wave propagation through the intact skull casts challenges. High-resolution numerical simulation allows for monitoring of the acoustic pressure field in the cranium but also demands extensive computational loads. In this study, we adopt a super-resolution convolutional neural network (SRCNN) technique to enhance the prediction quality of the FUS acoustic pressure field in the targeted brain regions. The training dataset was acquired by numerical simulations performed at low-(1.0 mm) and high-resolutions (0.5 mm) on three ex vivo human calvaria. Five different 3D SRCNN models were trained by using a multivariable dataset, which incorporated information on the acoustic pressure field, wave velocity, and skull computed tomography (CT) images. We achieved an accuracy of 80.87±4.50% in predicting the focal volume with a substantial improvement of 86.91% in computational cost compared to the conventional high-resolution numerical simulation. The results suggest that the method can greatly reduce the simulation time without sacrificing accuracy. </p>

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A conservative numerical scheme for modeling nonlinear acoustic propagation in thermoviscous homogeneous media

Cited by 18 publications

References 26 publications

A Holistic Physics-Informed Neural Network Solution for Precise Destruction of Breast Tumors using High-Intensity Focused Ultrasound on a Realistic Breast Model

A Holistic Physics-Informed Neural Network Solution for Precise Destruction of Breast Tumors using High-Intensity Focused Ultrasound on a Realistic Breast Model

GPU-accelerated study of the inertial cavitation threshold in viscoelastic soft tissue using a dual-frequency driving signal

Multivariable-Incorporating Super-Resolution Convolutional Neural Network for Transcranial Focused Ultrasound Simulation

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