Firuzalizadeh Maryam, Gaffoglio Rossella, Giordanengo Giorgio, Righero Marco, Vecchi Giuseppe
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.
Advanced Computing, Photonics & Electromagnetics (CPE) Area, Fondazione LINKS, 10138 Turin, Italy.
Cancers (Basel). 2025 Aug 28;17(17):2813. doi: 10.3390/cancers17172813.
Microwave hyperthermia is a clinically validated adjunctive therapy in oncology, employing antenna applicators to selectively raise tumor tissue temperature to 40-44 °C. For deep-seated tumors, especially those in anatomically complex areas like the head and neck (H&N) region, phased array antennas are typically employed. Determining optimal antenna feeding coefficients is crucial to maximize the specific absorption rate (SAR) within the tumor and minimize hotspots in healthy tissues. Conventionally, this optimization relies on meta-heuristic global algorithms such as particle swarm optimization (PSO).
In this study, we consider a deterministic alternative to PSO in microwave hyperthermia SAR-based optimization, which is based on the Alternating Projections Algorithm (APA). This method iteratively projects the electric field distribution onto a set of constraints to shape the power deposition within a predefined mask, enforcing SAR focusing within the tumor while actively suppressing deposition in healthy tissues. To address the challenge of selecting appropriate power levels, we introduce an adaptive power threshold search mechanism using a properly defined quality parameter, which quantifies the excess of deposited power in healthy tissues.
The proposed method is validated on both a simplified numerical testbed and a realistic anatomical phantom. Results demonstrate that the proposed method achieves heating quality comparable to PSO in terms of tumor targeting, while significantly improving hotspot suppression.
The proposed APA framework offers a fast and effective deterministic alternative to meta-heuristic methods, enabling SAR-based optimization in microwave hyperthermia with improved tumor targeting and enhanced suppression of hotspots in healthy tissue.
微波热疗是肿瘤学中一种经过临床验证的辅助治疗方法，使用天线 applicators 将肿瘤组织温度选择性地升高到 40 - 44°C。对于深部肿瘤，尤其是头颈部（H&N）等解剖结构复杂区域的肿瘤，通常采用相控阵天线。确定最佳天线馈电系数对于最大化肿瘤内的比吸收率（SAR）并最小化健康组织中的热点至关重要。传统上，这种优化依赖于元启发式全局算法，如粒子群优化（PSO）。
在本研究中，我们考虑在基于微波热疗 SAR 的优化中采用一种替代 PSO 的确定性方法，该方法基于交替投影算法（APA）。此方法将电场分布迭代投影到一组约束条件上，以在预定义掩码内塑造功率沉积，在肿瘤内实现 SAR 聚焦，同时积极抑制健康组织中的沉积。为应对选择合适功率水平的挑战，我们引入了一种自适应功率阈值搜索机制，使用适当定义的质量参数，该参数量化健康组织中沉积功率的过量情况。
所提出的方法在简化的数值测试平台和逼真的解剖模型上均得到验证。结果表明，所提出的方法在肿瘤靶向方面实现了与 PSO 相当的加热质量，同时显著改善了热点抑制。
所提出的 APA 框架为元启发式方法提供了一种快速有效的确定性替代方案，能够在微波热疗中基于 SAR 进行优化，提高肿瘤靶向性并增强对健康组织中热点的抑制。
