基于周期性加热模式的激光消融组织效果快速预测及优化设计方法

Rapid Prediction and Optimization Design Method of Laser⁃Ablation Effect Based on Periodic Heating Mode

激光消融技术以其高精度、强可控性和微创性等优势,在癌症治疗领域中展现出广阔的应用前景。为确保在肿瘤组织被完全消融的同时,激光消融技术对正常组织的热损伤最小,提出了一种基于周期性加热模式的辐照参数优化设计方法。首先,建立激光消融过程中生物传热和热损伤的数值模拟模型,通过实验验证该模型的准确性。随后,利用径向基函数神经网络建立激光消融效果预测模型,以快速、精准地评估正常组织的不可逆热损伤体积、不可逆热损伤总体积,以及肿瘤组织表面的平均温度。最后,采用遗传算法开展激光消融技术的单目标优化设计,在肿瘤组织完全消融和肿瘤组织表面的平均温度不超过70℃的前提下,正常组织的不可逆热损伤体积仅占不可逆热损伤总体积的0.053%,实现了激光消融技术对正常组织的不可逆热损伤的最小化目标。

Objective Laser ablation,owing to its advantages of high precision,strong controllability,and minimal invasiveness,is utilized widely for cancer treatment.However,this technology is affected by the elevated surface temperatures of biological tissues and the potential of irreversible thermal damage to adjacent normal tissues in clinical applications.To mitigate surface overheating in biological tissues,researchers have proposed employing forced convection cooling,implementing periodic heating patterns,and integrating laser-energy-modulation techniques to regulate laser-energy delivery.To address irreversible thermal damage to normal tissues,a prevalent strategy is the intratumoral injection of gold nanoparticles to significantly enhance laser-light absorption.Researchers have investigated various types of gold nanoparticles as near-infrared laser absorbers as well as the effects of parameters,such as laser settings,irradiation duration,and gold-nanoparticle volume fraction,on the efficacy of laser ablation.Nevertheless,previous studies have rarely focused on the optimization of laser-irradiation parameters using optimization algorithms without relying on external agents such as gold nanoparticles or other photosensitizers to ensure the complete eradication of tumor tissues while minimizing irreversible thermal harm to normal tissues.Methods In this study,we devise a method to rapidly predict and optimize laser-ablation effects via a cyclic heating mode.Initially,we simulate the heat distribution from laser photons in biological tissues using the Monte Carlo method.Subsequently,we employ the Pennes biological heat-transfer model and the Arrhenius model in simulations to obtain the temperature distribution and thermal-damage profiles of the biological tissues.To validate the accuracy of our established numerical simulation method for solving biological heat transfer and thermal damage,we construct an experimental platform for the laser irradiation of isolated porcine liver tissue(Fig.7).Subsequently,we conduct Latin hypercube sampling within the design variable space of the laser-ablation irradiation parameters.Next,we perform a numerical simulation to solve for the parameters related to the laser-ablation effect,including the irreversible thermal-damage volume of normal tissues,the total irreversible thermal damage volume,and the average surface temperature of tumor tissues.We utilize a radial-basis-function neural network to model the relationship between the irradiation and laser-ablation effect parameters,thus facilitating the rapid and accurate prediction of the laser-ablation effect.Finally,we employ a genetic algorithm to address the single-objective optimization problem.By optimizing the laser power,heating-area radius,heating and cooling times,and number of cycles,we propose an optimal design for the laser-ablation effect,which can minimize irreversible thermal damage to normal tissues while ensuring the complete eradication of tumor tissues and maintaining an acceptable surface temperature in biological tissues.Results and Discussions In this study,we validated the accuracy of a numerical simulation method for biological heat transfer and thermal damage by performing experiments involving the laser irradiation of isolated pig liver tissues.A comparative analysis of the numerical simulation and experimental results of temperature at the central point of the porcine liver-tissue surface under varying irradiation times revealed relative errors within 5%,with a maximum relative error of 3.07%(Fig.8).Similarly,the examination of the simulation and experimental results pertaining to the thermal damage volume of pig liver tissues across different laser irradiation times indicated the most significant deviation at 200 s,with a maximum error of 0.06 cm 3(Fig.9).Furthermore,we developed an agent model based on a radial-basis-function neural network,which enables the rapid and accurate prediction of laser-ablation effects.Laser-ablation effects were predicted based on 100 test samples under different irradiation design parameters within 4 s on a single thread of an Intel(R)Core(TM)i7-8700K central processing unit(CPU)operating at a main frequency of 3.70 GHz,with each test sample requiring only approximately 0.04 s.This computation time was significantly shorter than that required by conventional numerical-simulation methods,i.e.,5‒15 min per test sample.Additionally,a strong correlation between the actual and predicted values of the test samples was observed,with correlation coefficients exceeding 99.9%and root mean square errors below 0.01%(Fig.10),thus underscoring the exceptional fitting efficacy of the established proxy model in predicting temperature and thermal damage in biological tissues.This method is promising for optimizing laser-ablation effects using genetic algorithms.By adopting a genetic algorithm to optimize irradiation design parameters in laser ablation,we obtained parameters that ensured the complete ablation of tumor tissues while maintaining the tumor-tissue surface temperature below 70℃.The irreversible thermal damage volume of normal tissues was minimized to 7.046×10-5 cm 3,which constitutes only 0.053%of the total irreversible thermal damage volume(Figs.11 and 12).Under the optimal irradiation design parameters,the laser power was 4.35 W,the radius of the laser irradiation area was 5.11 mm,the heating time per cycle was 47.90 s,the cooling time per cycle was 62.45 s,and the number of cycles was 10.Conclusions In this study,a numerical simulation method for solving biological heat transfer and thermal damage in laser ablation was established,and the accuracy of the simulation method was verified experimentally.A method for predicting the laser-ablation effect was developed based on a radial-basis-function neural network that can rapidly and accurately evaluate the irreversible thermal damage volume of normal tissues,the total irreversible thermal damage volume,and the average temperature of the surface of tumor tissues.Additionally,a genetic algorithm was employed to conduct a single-objective optimal design of the laser-ablation technique.The optimized design minimized the irreversible thermal damage volume of normal tissues to 0.053%of the total irreversible thermal damage volume while ensuring the complete ablation of tumor tissues and maintaining the average temperature of the tumor-tissue surface below 70℃.