烧蚀
不可逆电穿孔
共形映射
胶质瘤
电穿孔
有限元法
脉搏(音乐)
电场
生物医学工程
材料科学
医学
电压
物理
心脏病学
癌症研究
数学
化学
生物化学
基因
热力学
数学分析
量子力学
作者
Lei Jiang,Lingchao Chen,Lujia Ding,Yongqin Yang,Shuangquan Yu,Zheng Fang,Zhiyong Qin,Bing Zhang
摘要
Abstract Background High‐frequency irreversible electroporation (H‐FIRE) has gradually become an attractive alternative treatment of intracranial tumors due to its clinically favorable characteristics, such as mild muscle contractions, precise ablation margins, and preservation of vessel structures. Encouraging results have been obtained in pre‐clinical trials with animal models. However, a more comprehensive understanding of spatiotemporal distributions of electric field and temperature in clinically relevant intracranial tissue during the treatment of H‐FIRE is still required prior to its clinical implementation. Purpose In this study, we performed the first attempt to numerically investigate the electric field and temperature distributions for the conformal ablation of intracranial tumors in patient‐specific glioma tumor models. Methods Four representative 3D patient‐specific glioma models were constructed based on T1‐weighted MR images of four clinical patients. The treatment protocols of H‐FIRE were optimized for the conformal ablation of these glioma patients by using a multi‐objective optimization genetic algorithm. To alleviate the temperature increase during the H‐FIRE administration, a new ablation procedure was designed and tested numerically. Results The results achieved in this study demonstrated that the conformal ablation of gliomas with differing sizes and shapes can be achieved by optimizing the number of electrodes, applied pulse voltage, active tip length, electrode gap, and electrode insertion depth. The temperature increases due to the administration of H‐FIRE pulses can be effectively alleviated by introducing a pulse‐off time between two ablation procedures. Conclusion This study contributes to the field of H‐FIRE in the treatment of intracranial tumors and promotes its clinical implementation.
科研通智能强力驱动
Strongly Powered by AbleSci AI