This comprehensive study combines several innovative approaches to enhance the precision and efficacy of high-intensity focused ultrasound (HIFU) for cancer treatment. HIFU, a non-invasive therapeutic technique, uses high-frequency ultrasound to ablate tumors, but requires careful planning due to its potential for collateral damage to healthy tissues. To overcome these challenges, multiple methodologies are introduced.

By employing a Physics-Informed Neural Network (PINN) integrated with a realistic breast model, breast tumors are targeted with high precision. The model utilizes a bowlshaped acoustic transducer to focus ultrasound waves directly on the tumor, achieving intense localized heating. The PINN method efficiently solves partial differential equations in a mesh-free domain, providing high accuracy with significantly lower computational demands than traditional finite element methods (FEM).

This model is employed to understand the governing dynamics of the HIFU process, particularly the heat transfer mechanisms during sonication. Using machine learning techniques, the model simulates the absorption mechanism and temperature rise, validated through ex vivo experiments with bovine liver. This helps in accurately predicting and visualizing the effects of treatment, facilitating the development of personalized treatment strategies.

A novel deep learning-based optimization procedure is used for preoperative treatment planning. It determines optimal focal locations and sonication times for each ablation session, ensuring minimal tissue over- or under-treatment. This algorithm has shown high potential in handling various HIFU presurgical plans and is especially effective in creating precise treatment plans based on patient-specific material properties.

Addressing the limitations of manual HIFU operations, a real-time, low-cost image segmentation framework based on the Swin-Unet architecture is proposed. This system is trained and tested on B-mode imaging simulations and real images from HIFU-treated chicken breast, demonstrating high efficiency in lesion segmentation and offering potential for monitored, automated HIFU therapy.

By integrating these diverse approaches, the study not only enhances the thermal effects of HIFU but also offers a framework for safer, more precise, and individually tailored cancer treatments. The combined use of advanced simulation models, deep learning approaches, and innovative technologies aims to significantly improve the therapeutic outcomes of HIFU, making it a more viable option for cancer treatment with reduced risks and enhanced efficacy.