A holistic physics-informed neural network solution for precise destruction of breast tumors using focused ultrasound...

This study presented a novel approach for the precise ablation of breast tumors using focused ultrasound (FUS), leveraging a physics-informed neural network (PINN) integrated with a realistic breast model. FUS has shown significant promise in treating breast tumors by effectively targeting and ablating cancerous tissue. 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 posed significant challenges. Our PINN-based solution operated 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 employed a bowl-shaped acoustic transducer to focus ultrasound waves accurately on the tumor location. The simulation results offered detailed insights into each step of the FUS treatment process, including the generation of acoustic waves, the targeting of the tumor, and the subsequent heating and ablation of cancerous tissue. By applying a 3.8 nm displacement amplitude of transducer input pulse at a frequency of 1.1 MHz for 1 second, the temperature at the focal point elevated to 38.4 ℃, followed by another 90 seconds of cooling time, which resulted in significant necrosis of the tumor tissues. Validation of the PINN model's accuracy was conducted through FEM analysis, aligning closely with real-world FUS therapy scenarios. This innovative model provided physicians with a predictive tool to estimate the necrosis of tumor tissue, facilitating the customization of FUS treatment strategies for individual breast cancer patients.