基于拓扑算法的肝脏肿瘤消融术前路径规划系统  

A preoperative path planning system for liver tumor ablation based on topological algorithms

作　　者：徐旭 朱文文 夏栋 巫彤宁 陈新华[2] 李从胜 XU Xu;ZHU Wenwen;XIA Dong;WU Tongning;CHEN Xinhua;LI Congsheng(China Telecommunication Technology Labs,China Academy of Information and Communications Technology,Beijing 100191;The First Affiliated Hospital,Zhejiang University,Hangzhou 310003)

机构地区：[1]中国信息通信研究院泰尔终端实验室,北京100191 [2]浙江大学附属第一医院,杭州310003

出　　处：《北京生物医学工程》2022年第2期111-117,133,共8页Beijing Biomedical Engineering

基　　金：国家重点研发计划(2019YFF0216302);国家自然科学基金(61971445);国家科技部重大专项(2018ZX10301201)资助。

摘　　要：目的探索提高肝癌消融术路径规划的准确性和肝癌消融术疗效的方法。方法研究结合病患个体解剖特征的多约束进针路径规划模型,优化符合病患病灶分布特征的术前方案。本文提出深度学习模型以提高患者腹部组织器官重建精度,并设计拓扑优化算法进行多约束进针路径规划。结果系统通过重建患者腹部多组织数字模型,分析病灶和周围组织位置关系,制定满足临床条件的进针路径。结论本文所提出的肝肿瘤消融术前路径规划,可实现融合病患个体特征的可行手术穿刺方案制定,临床40例肝肿瘤患者术后增强影像显示病灶均完全消融,技术有效率达100%。Objective Explore ways to improve the accuracy of ablation pathway planning and the efficacy of ablation for hepatocellular carcinoma.Methods A multi-constrained approach path planning model incorporating individual patient anatomical features is investigated to optimize a preoperative plan that matches the patient’s lesion distribution characteristics.We propose a deep learning model to improve the accuracy of the patient’s abdominal tissue and organ reconstruction,and design a topology optimization algorithm for multi-constrained approach path planning.Results By reconstructing a multi-tissue digital model of the patient’s abdomen,the system analyses the relationship between the location of the lesion and the surrounding tissues and develops a needle path that meets the clinical conditions.Conclusions The preoperative pathway planning for liver tumor ablation proposed in this paper enables the development of a feasible surgical puncture plan that incorporates individual patient characteristics.Fourty patients with liver tumors are completely ablated on postoperative enhanced imaging,with a technical efficiency of 100%.

关 键 词：肝肿瘤 计算机辅助 消融针 路径规划 临床约束条件 

分 类 号：R318.04[医药卫生—生物医学工程]