The Characterization and Assembly of an Efficient, Cost Effective Focused Ultrasound Transducer

Maslakowski, Michael S.; Ilham, Sheikh Jawad; Hall, Timothy L.; Subramanian, Thyagarajan; Kiani, Mehdi; Almekkawy, Mohamed

doi:10.1109/dcas51144.2020.9330669

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…This underlying data may be used as a regularization factor to limit the space of potential solutions to a field domain that is allowable in the desired calculations. Incorporating this sort of structured data into a learning algorithm may improve performance by boosting the data's functional content, allowing it to find the best possible answer with little training data and generalize effectively 53,54 .…”

Section: Model-based Neural Networkmentioning

confidence: 99%

Model Based Deep Learning Method for Focused Ultrasound Pathway Scanning

Lari,

Kohandel,

Kwon

2024

Preprint

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The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is to precisely target and ablate tumors by focusing high-frequency ultrasound from an external power source. A series of ablations must be performed in order to treat a big volume of tumors, as a single ablation can only remove a small amount of tissue. To maximize therapeutic efficacy while minimizing adverse side effects such as skin burns, preoperative treatment planning is essential in determining the focal site and sonication duration for each ablation. Here, we introduce a machine learning-based approach for designing HIFU treatment plans, which makes use of a map of the material characteristics unique to a patient alongside an accurate thermal simulation. A numerical model was employed to solve the governing equations of HIFU process and to simulate the HIFU absorption mechanism, including ensuing heat transfer process and the temperature rise during the sonication period. To validate the accuracy of this numerical model, a series of tests was conducted using ex vivo bovine liver. The findings indicate that the developed models properly represent the considerable variances observed in tumor geometrical shapes and proficiently generate well-defined closed treated regions based on imaging data. The proposed strategy facilitated the formulation of high-quality treatment plans, with an average tissue over-or under-treatment rate of less than 0.06 percent. The efficacy of the numerical model in accurately predicting the heating process of HIFU, when combined with machine learning techniques, was validated through quantitative comparison with experimental data. The proposed approach in cooperation with HIFU simulation holds the potential to enhance presurgical HIFU plan.

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Section: Model-based Neural Networkmentioning

confidence: 99%

Model Based Deep Learning Method for Focused Ultrasound Pathway Scanning

Lari,

Kohandel,

Kwon

2024

Preprint

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“…PINNs have revolutionized the approach to solving nonlinear partial differential equations (PDEs) by leveraging deep learning to approximate solutions while adhering to physical laws, reducing computational demands significantly compared to traditional methods like finite difference method (FDM) and finite element method (FEM) [25,26,27,28,29,30,31,32,33,34]. This study utilizes a 3D model based on an anatomically realistic breast phantom (ARBP) generated from T1-weighted MRIs to reflect the breast's complex anatomy accurately, enhancing the precision and clinical relevance of HIFU simulations.…”

Section: Introductionmentioning

confidence: 99%

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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