Liu H, Su Z, Huang C, Zhao L, Chen Y, Zhou Y, Lü H, Feng Q
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Sep 20;44(9):1783-1795. doi: 10.12122/j.issn.1673-4254.2024.09.19.
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.
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.
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, D, and Depth ( < 0.05) but much better D, D, D, and A ( < 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).
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.
尽量减少因医生技术水平差异导致的L5/S1经皮椎间孔射频热凝术（PIRFT）治疗结果的差异，并实现穿刺路径的精确量化风险评估。
我们使用自行开发的深度神经网络DWT-UNet将L5/S1节段的磁共振（MR）图像自动分割为7个关键结构：L5、S1、髂骨、椎间盘、神经根5、硬脑膜和皮肤，并在此基础上建立针插入路径规划环境。根据临床针插入标准提出了6个硬约束和6个软约束，并使用层次分析法将医生的经验量化为权重，纳入针插入路径的风险函数中，以增强对个别病例的适应性。通过结合所提出的皮肤入口点采样子算法和坎宾三角投影区域子算法，并运用层次分析法，采用光线追踪、CPU多线程和GPU并行计算等多种技术，计算出不仅满足临床硬约束而且优化了整体软约束的穿刺路径。
一个手术团队对算法规划的21条针穿刺路径进行了主观评估，所有路径均符合临床要求，其中95.24%被评为优秀或良好。与医生的规划结果相比，算法生成的方案在D、D和深度方面较差（<0.05），但在D、D、D和A方面要好得多（<0.05）。在这21例病例中，算法的规划时间平均为7.97±3.73秒，远短于医生的规划时间（通常超过10分钟）。
多约束最优穿刺路径规划算法为L5/S1节段的PIRFT提供了一种高效的自动化解决方案，具有很大的临床应用潜力。
