摘要
目的为有效减少L5/S1微创椎间盘射频消融术中因医师操作熟练度差异导致的治疗效果波动,实现对消融针穿刺路径风险的精确量化评估。方法基于自主研发的深度神经网络DWT-UNet,将L5/S1节段的MR图像精准分割为7个关键结构:L5椎骨,S1椎骨,髂骨,L5/S1节段的椎间盘、神经根、硬脊膜和皮肤。基于上述分割结果,3D建模穿刺路径的规划环境。针对穿刺的临床准则提出了6个硬性约束和6个软性约束,将医师经验通过层次分析算法量化为权重添加到穿刺路径风险函数中以提升对个例的适应性。通过提出的皮肤进针点采样子算法、Kambin三角投影面积子算法,结合层次分析算法,并运用光线追踪、CPU多线程处理和GPU并行计算等多种技术,计算出一条既符合临床硬性约束条件,又能使软性约束条件综合达到最优的穿刺路径。结果医师团队对算法规划的21例消融针穿刺路径进行主观评估,结果显示所有路径均满足临床基本要求(及格率100%),且95.24%的路径被评为优秀或良好。在与医师规划结果的对比中,算法在D_(Ilium)、D_(S1)和Depth等3个指标上有不同程度的下降(P<0.05),在D_(Dura)、D_(L5)、D_(N5)和A_(Kambin)等4个指标上有较大提升(P<0.05)。算法规划21例的平均时间为7.97±3.73 s,而医师传统规划普遍需耗时10 min以上。结论本研究提出的多约束最优穿刺路径规划算法为L5/S1节段的微创椎间盘射频消融术提供了一种一站式的解决方案。经过严谨的临床评价和实验验证,该算法显示出独特的优势和广阔的临床应用潜力。
Objective To minimize variations in treatment outcomes of L5/S1 percutaneous intervertebral radiofrequency thermocoagulation(PIRFT)arising from physician proficiency and achieve precise quantitative risk assessment of the puncture paths.Methods We used a self-developed deep neural network DWT-UNet for automatic segmentation of the magnetic resonance(MR)images of the L5/S1 segments into 7 key structures:L5,S1,Ilium,Disc,N5,Dura mater,and Skin,based on which a needle insertion path planning environment was modeled.Six hard constraints and 6 soft constraints were proposed based on clinical criteria for needle insertion,and the physician's experience was quantified into weights using the analytic hierarchy process and incorporated into the risk function for needle insertion paths to enhance individual case adaptability.By leveraging the proposed skin entry point sampling sub-algorithm and Kambin's triangle projection area sub-algorithm in conjunction with the analytic hierarchy process,and employing various technologies such as ray tracing,CPU multi-threading,and GPU parallel computing,a puncture path was calculated that not only met clinical hard constraints but also optimized the overall soft constraints.Results A surgical team conducted a subjective evaluation of the 21 needle puncture paths planned by the algorithm,and all the paths met the clinical requirements,with 95.24%of them rated excellent or good.Compared with the physician's planning results,the plans generated by the algorithm showed inferior D_(Ilium),D_(S1),and Depth(P<0.05)but much better D_(Dura),D_(L5),D_(N5),and A_(Kambin)(P<0.05).In the 21 cases,the planning time of the algorithm averaged 7.97±3.73 s,much shorter than that by the physicians(typically beyond 10 min).Conclusion The multi-constraint optimal puncture path planning algorithm offers an efficient automated solution for PIRFT of the L5/S1 segments with great potentials for clinical application.
作者
刘虎
苏志海
黄成颉
赵磊
陈扬帆
周宇佳
吕海
冯前进
LIU Hu;SU Zhihai;HUANG Chengjie;ZHAO Lei;CHEN Yangfan;ZHOU Yujia;LÜHai;FENG Qianjin(School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Guangdong Provincial Key Laboratory of Medical Image Processing,Guangzhou 510515,China;Department of Spinal Surgery,Fifth Affiliated Hospital of Sun Yat-sen University,Zhuhai 519000,China)
出处
《南方医科大学学报》
CAS
CSCD
北大核心
2024年第9期1783-1795,共13页
Journal of Southern Medical University
基金
国家自然科学基金(12126603)
琶洲实验室研发项目(2023K0604)。