Elements Selection for Transcostal HIFU Refocusing Method: Simulation Study

McMahon, Daniel; Almekkawy, Mohamed

doi:10.1109/tuffc.2020.2973678

Cited by 14 publications

(2 citation statements)

References 43 publications

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“…The literature on HIFU has been constrained by a lack of comprehensive 3D modeling and the absence of studies using anatomically accurate breast phantoms due to their complexity. To bridge this gap, leveraging machine learning and deep neural networks has become increasingly effective, extending to scientific computing where they facili-tate differential and integral calculations, offering an alternative to traditional numerical methods [15,16,17,18,19,20,21,22,23]. This shift towards employing deep learning for obtaining numerical solutions and handling the challenges of data acquisition in the medical field has led to the development of physics-informed neural networks (PINNs), which train neural networks to comply with the physics laws governing the problems, thus enabling the simulation of realistic clinical procedures and detailed examination of HIFU ablation processes [24].…”

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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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 surgical solution is in flagrant contradiction with the minimally-invasive nature of the intervention but offers a solution when no other treatments are practicable. Another approach was the development of software method such as de-activation of chosen elements based on shadowing [22] or algorithms that consider the diffraction and interferences to maximize the energy deposited at the focus versus the ribs [23] , [24] . Element selection demonstrated benefits for reduction of side lobes and bone heating, as well as better focusing despite reasonable loss of energy deposition at the focus.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Intra-Operatory Positioning of an MRgHIFU Applicator Dedicated to Abdominal Thermal Therapy Using Particle Swarm Optimization

M'Rad,

Charbonnier,

de Oliveira

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Purpose: Transducer positioning for liver ablation by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is challenging due to the presence of air-filled organs or bones on the beam path. This paper presents a software tool developed to optimize the positioning of a HIFU transducer dedicated to abdominal thermal therapy, to maximize the treatment's efficiency while minimizing the near-field risk. Methods: A software tool was developed to determine the theoretical optimal position (TOP) of the transducer based on the minimization of a cost function using the particle swarm optimization (PSO). After an initialization phase and a manual segmentation of the abdomen of 5 pigs, the program randomly generates particles with 2 degrees of freedom and iteratively minimizes the cost function of the particles considering 3 parameters weighted according to their criticality. New particles are generated around the best position obtained at the previous step and the process is repeated until the optimal position of the transducer is reached. MR imaging data from in vivo HIFU ablation in pig livers was used for ground truth comparison between the TOP and the experimental position (EP). Results: As compared to the manual EP, the rotation difference with the TOP was on average −3.1 ± 7.1° and the distance difference was on average −7.1 ± 5.4 mm. The computational time to suggest the TOP was 20s. The software tool is modulable and demonstrated consistency and robustness when repeating the calculation and changing the initial position of the transducer.

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

Maslakowski

Ilham

Hall

et al. 2020

2020 IEEE 14th Dallas Circuits and Systems Conference (DCAS)

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Elements Selection for Transcostal HIFU Refocusing Method: Simulation Study

Cited by 14 publications

References 43 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

Computer-Aided Intra-Operatory Positioning of an MRgHIFU Applicator Dedicated to Abdominal Thermal Therapy Using Particle Swarm Optimization

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

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