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Objective: High intensity focused ultrasound (HIFU) is a new noninvasive therapeutics that allows local treatment of solid tumors through a hyperthermal mechanism using ultrasonic energy. One promising strategy to increase the thermal efficiency of HIFU is to employ nanoparticles (NPs) as ultrasound agents for the hyperthermia procedure. However, the interaction mechanism between NPs and ultrasonic waves has not been well understood. Methods: In an effort to investigate the heating process of NPs-enhanced HIFU, we derived a set of HIFU equations governing the temperature variation during the thermal ablation based on the principle of conservation of energy for heat transfer mechanism. A numerical model was developed to solve the HIFU equations to simulate the absorption mechanism of HIFU in the presence of NPs, the consequent heat transfer process, and the temperature rise profile during the sonication period. The accuracy of numerical model was verified by performing a series of experiments on tissue-mimicking phantoms embedded with magnetic NPs (MNPs). Results: The transport processes taking place at the boundaries between NPs and surrounding medium played the major role in the temperature rise during HIFU sonication. Besides, the effects of MNPs on rising temperature were improved by amplifying the ultrasonic power and frequency as well as by increasing the MNP concentration. Conclusion: A quantitative comparison with experimental results demonstrated the potential of the numerical model to accurately predict the heating mechanism of HIFU mediated by NPs. Significance: The proposed method can help with simulation of HIFU when NPs are employed as ultrasound agents. [ABSTRACT FROM AUTHOR]

• A novel method for monitoring focused ultrasound (FUS) is proposed. • Temperature rise at the focused region can be detected by the breast thermography. • The inverse analysis of thermal images can lead to quantitative measurement of FUS's thermal parameters. • Intelligent thermography can be used as a real-time method for monitoring FUS. Focused ultrasound (FUS) therapy has been widely studied for breast cancer treatment due to its potential as a fully non-invasive method to improve cosmetic and oncologic results. However, real-time imaging and monitoring of the therapeutic ultrasound delivered to the target area remain challenges for precision breast cancer therapy. The main objective of this study is to propose and evaluate a novel intelligence-based thermography (IT) method that can monitor and control FUS treatment using thermal imaging with the fusion of artificial intelligence (AI) and advanced heat transfer modeling. In the proposed method, a thermal camera is integrated into FUS system for thermal imaging of the breast surface, and an AI model is employed for the inverse analysis of the surface thermal monitoring, thereby estimating the features of the focal region. This paper presents experimental and computational studies conducted to assess the feasibility and efficiency of IT-guided FUS (ITgFUS). Tissue phantoms, designed to mimic the properties of breast tissue, were used in the experiments to investigate detectability and the impact of temperature rise at the focal region on the tissue surface. Additionally, an AI computational analysis employing an artificial neural network (ANN) and FUS simulation was carried out to provide a quantitative estimation of the temperature rise at the focal region. This estimation was based on the observed temperature profile on the breast model's surface. The results proved that the effects of temperature rise at the focused area could be detected by the thermal images acquired with thermography. Moreover, it was demonstrated that the AI analysis of the surface temperature measurement could result in near real-time monitoring of FUS by quantitative estimation of the temporal and spatial temperature rise profiles at the focal region. [ABSTRACT FROM AUTHOR]

High-intensity focused ultrasound (HIFU) is a non-invasive medical procedure, which is mainly used to ablate tumors externally by focusing on them with high-frequency ultrasound. Because a single ablation can process only a small volume of tissue, a succession of ablations is required to treat a large volume of cancerous tissue. In order to maximize the therapeutic effect and reduce side effects such as skin burns, careful preoperative treatment planning must be performed to determine the focal location and sonication time for each ablation. This paper proposes a novel optimization algorithm, called the thermodynamic equilibrium algorithm (TEA), inspired by the behavior of thermodynamic systems reaching their equilibrium states. Like other evolutionary algorithms, TEA starts with an initial population. Gas chambers at various thermodynamic states are employed as representatives of the population individuals, and the equilibrium state is regarded as the global minimum. The movement of thermodynamic parameters in the direction of reducing the temperature gradient forms the basis of the proposed evolutionary algorithm. During this movement, the second law of thermodynamics is checked to ensure that entropy will increase in each process. This movement leads to the state where most of the systems are at equilibrium. In this state, the systems are localized at the same position and have the same cost as the global minimum. The TEA was applied to several well-known unconstrained and constrained benchmark cost functions, and the performance was compared with other well-known optimization algorithms. The results showed that the TEA has high potential to handle various types of optimization problems with a good convergence rate and high precision. Finally, the suggested evolutionary approach is applied to HIFU treatment regimens adopting a map of patient-specific material properties and an accurate thermal model. High-quality treatment plans could be created using the suggested method, and the average amount of tissue that is over- or under-treated was less than 0.08 percent. [ABSTRACT FROM AUTHOR]