面向L5/S1微创椎间盘射频消融术的多约束最优穿刺路径规划算法

doi:10.12122/j.issn.1673-4254.2024.09.19

南方医科大学学报 ›› 2024, Vol. 44 ›› Issue (9): 1783-1795.doi: 10.12122/j.issn.1673-4254.2024.09.19

面向L5/S1微创椎间盘射频消融术的多约束最优穿刺路径规划算法

刘虎¹^,²(), 苏志海³, 黄成颉³, 赵磊¹^,², 陈扬帆¹^,², 周宇佳¹^,², 吕海³, 冯前进¹^,²()

^1.南方医科大学生物医学工程学院，广东广州 510515
^2.广东省医学图像处理重点实验室，广东广州 510515
^3.中山大学附属第五医院脊柱外科，广东珠海 519000

收稿日期:2024-06-14 出版日期:2024-09-20 发布日期:2024-09-30
通讯作者: 冯前进 E-mail:liuhu_smu@outlook.com;fengqj99@smu.edu.cn
作者简介:刘虎，在读硕士研究生，E-mail: liuhu_smu@outlook.com
基金资助:
国家自然科学基金(12126603);琶洲实验室研发项目(2023K0604)

A multi-constraint optimal puncture path planning algorithm for percutaneous interventional radiofrequency thermal fusion of the L5/S1 segments

Hu LIU¹^,²(), Zhihai SU³, Chengjie HUANG³, Lei ZHAO¹^,², Yangfan CHEN¹^,², Yujia ZHOU¹^,², Hai LÜ³, Qianjin FENG¹^,²()

^1.School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
^2.Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China
^3.Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China

Received:2024-06-14 Online:2024-09-20 Published:2024-09-30
Contact: Qianjin FENG E-mail:liuhu_smu@outlook.com;fengqj99@smu.edu.cn
Supported by:
National Natural Science Foundation of China(12126603)

摘要/Abstract

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。

关键词: 微创椎间盘射频消融术, L5/S1, Kambin三角, 路径规划

Abstract:

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.

Key words: percutaneous intervertebral radiofrequency thermocoagulation, L5/S1, Kambin's triangle, path planning

刘虎, 苏志海, 黄成颉, 赵磊, 陈扬帆, 周宇佳, 吕海, 冯前进. 面向L5/S1微创椎间盘射频消融术的多约束最优穿刺路径规划算法[J]. 南方医科大学学报, 2024, 44(9): 1783-1795.

Hu LIU, Zhihai SU, Chengjie HUANG, Lei ZHAO, Yangfan CHEN, Yujia ZHOU, Hai LÜ, Qianjin FENG. A multi-constraint optimal puncture path planning algorithm for percutaneous interventional radiofrequency thermal fusion of the L5/S1 segments[J]. Journal of Southern Medical University, 2024, 44(9): 1783-1795.

图/表 20

图1 3D路径规划环境模型

Fig.1 3D path planning environment.

图2 算法的整体流程

Fig.2 Overall workflow of the algorithm.

图3 DWT-UNet模型结构图

Fig.3 Structural diagram of DWT-UNet model.

表1 PIRFT穿刺路径规划的约束

Tab.1 Constraints for path planning in radiofrequency ablation for lumbar disc herniation

ID	Hard constraints	Soft constraints
A	To avoid collision with the risk-prone anatomical structures in the L5/S1 segment (including L5, S1, N5, dura mater, and ilium), ensuring a minimum clearance of 0.5mm (considering the outer diameter of a 20G needle is approximately 0.9mm) is crucial to maintain a safety margin during the procedure.^{[14, 16]}	The distance between the puncture path and critical anatomical structures should be maximized as much as possible.^[14]
B	The puncture depth should be less than 150mm (considering the length of the puncture needle is 150mm) to ensure the ablation target can be effectively reached.^{[14, 16]}	The puncture depth should be minimized as much as possible.^[14]
C	To prevent the phenomenon of needle slippage, the angle between the puncture path and the surface of the intervertebral disc should be greater than 20 degrees.^{[14, 16]}	The angle between the puncture path and the surface of the intervertebral disc should be maximized as much as possible.^[14]
D*	The puncture path must traverse the intervertebral foramen.	Utilize a distance field based on bone structures to navigate and avoid densely packed bone regions.
E*	The puncture should be performed from the posterior (back) side.	Utilize a distance field based on neural tissue to navigate and avoid densely populated neural regions.
F*	The entry point of the puncture path must be located on the skin overlying the L5/S1 segment, in order to restrict the insertion range of the ablation needle.	The projected area of the Kambin's triangle should be maximized as much as possible.

图4 违反消融针穿刺硬性约束示例图

Fig.4 Illustration of violations of hard constraints for radiofrequency ablation needle puncture. A: Collision between puncture path and S1. B: Depth greater than the maximum length. C: Entry point out of range. D: Excessively small angle. E: Path misses intervertebral foramen. F: Puncture from anterior approach.

图5 路径到关键结构示意图

Fig.5 Diagram of the path to the key structures.

图6 Kambin三角的三轴极限视角图

Fig.6 Diagram of the XYZ Axis limited perspective of the Kambin's triangle.

图7 Kambin三角投影示例

Fig.7 Kambin's triangle projection. A: Kambin's triangle projection area. B: Extracted Kambin's triangle region after grayscale conversion.

图8 不可行穿刺区域示例图

Fig.8 Diagram of non-feasible puncture area.

表2 准则层的判断矩阵

Tab.2 Judgment matrix for criterion layer

Decision criteria	W_Ilium	W_Dura	W_L5	W_N5	W_S1	W_Depth	W_Kambin
W_Ilium	1	1/3	1/3	1/3	1/3	3	1/7
W_Dura	3	1	1/3	3	3	5	1/3
W_L5	3	3	1	5	5	7	1/3
W_N5	3	1/3	1/5	1	3	5	1/5
W_S1	3	1/3	1/5	1/3	1	5	1/7
W_Depth	1/3	1/5	1/7	1/5	1/5	1	1/9
W_Kambin	7	3	3	5	7	9	1

图9 权重分布复合饼柱图

Fig.9 Chart of the weight distribution.

表3 皮肤进针点采样算法

Tab.3 Algorithm for skin entry point sampling

Algorithm 1 skin insertion point sampling algorithm

Input:Local 3D model of skin S

Output:Uniform sampling needle insertion point on the skin

1:for triangle ABC in S

2: // calculate the two edge vectors of triangle ABC

3: $V e c t o r A B = P A – P B$

4: $V e c t o r A C = P A – P C$

5: // calculate the area of triangle ABC

6: $A r e a = 12 * A B × A C$

7: s $a m p l e s i z e N = 5 * A r e a$

8: for j = 1, 2, 3, …, N do

9: // generate random numbers $r 1$ and $r 2$ within the range of $[0,1]$

10: if $r 1 + r 2 > 1$ :

11: $r 1 = 1 - r 1$

12: $r 2 = 1 - r 2$

13: end if

14: // sample points in triangle ABC

15: $P j = P A + r 1 * V e c t o r A B + r 2 * V e c t o r A C$

16: add $P j$ to the sampling point list

17: end for

18: end for

19: return sampling point list

表3 皮肤进针点采样算法

Tab.3 Algorithm for skin entry point sampling

Algorithm 1 skin insertion point sampling algorithm

Input:Local 3D model of skin S

Output:Uniform sampling needle insertion point on the skin

1:for triangle ABC in S

2: // calculate the two edge vectors of triangle ABC

3: $V e c t o r A B = P A – P B$

4: $V e c t o r A C = P A – P C$

5: // calculate the area of triangle ABC

6: $A r e a = 12 * A B × A C$

7: s $a m p l e s i z e N = 5 * A r e a$

8: for j = 1, 2, 3, …, N do

9: // generate random numbers $r 1$ and $r 2$ within the range of $[0,1]$

10: if $r 1 + r 2 > 1$ :

11: $r 1 = 1 - r 1$

12: $r 2 = 1 - r 2$

13: end if

14: // sample points in triangle ABC

15: $P j = P A + r 1 * V e c t o r A B + r 2 * V e c t o r A C$

16: add $P j$ to the sampling point list

17: end for

18: end for

19: return sampling point list

图10 误差示意图

Fig.10 Diagram of error.

表4 分级核心标准

Tab.4 Core criteria for grading

Level	Core criteria
Excellent	Satisfy all clinical hard constraints and D_Dura, D_N5, D_L5, Depth, A_Kambin all outperforme the manual reference path, where Depth and A_Kambinare allowed to have a downward deviation of no more than 2%.
Good	Satisfy all clinical hard constraints, with D_N5 and Depth required to outperform the manual reference path, while D_Dura, D_L5 and A_Kambin are allowed to have a downward deviation of no more than 5%.
Moderate	Satisfy all clinical hard constraints and at least 3 out of the 7 indicators (D_Dura, D_N5, D_L5, Depth, A_Kambin, Depth, A_Kambin) outperforme the manual reference path.
Pass	Satisfy all clinical hard constraints.
Fail	Violation of any clinical hard constraint condition (Tab.1).

表5 各结构的不同模型分割DSC结果

Tab.5 DSC of each structure segmented using different models

Model	L5/S1
Model	S1	Disc	L5	Dura mater	N5
nnFormer^[29]	0.9434	0.9219	0.9400	0.9029	0.7933
DeepLabv3^[30]	0.8070	0.8701	0.8427	0.8161	0.5073
U-Net++^[31]	0.9462	0.9225	0.9419	0.9066	0.8012
DWT-UNet (Ours)	0.9491	0.9373	0.9488	0.9197	0.8274

表6 消融实验结果

Tab.6 DSC of the ablation experiment

Model	L5/S1
Model	S1	Disc	L5	Dura mater	N5
3D U-Net^[24]	0.9453	0.9231	0.9413	0.9035	0.7981
3D U-Net+DWT	0.9491	0.9373	0.9488	0.9197	0.8274
ResUNet^[32]	0.9429	0.9221	0.9400	0.8974	0.7868
ResUNet+DWT	0.9466	0.9346	0.9477	0.9111	0.8160
V-Net^[33]	0.9404	0.9226	0.9356	0.8944	0.7814
V-Net+DWT	0.9426	0.9269	0.9431	0.9054	0.7974

表7 算法规划的21例穿刺路径结果

Tab.7 Parameters of the puncture paths planned by the algorithm in 21 cases

ID	Gender	Age (year)	Initial candidate needle insertion points	Needle insertion points that satisfy hard constraints	Depth (mm)	Clinical level
Case1	Female	53	43 035	22 097	123.71	Good
Case2	Female	54	38 040	18 362	113.90	Good
Case3	Male	39	43 925	11 689	126.46	Good
Case4	Male	40	44 285	12 935	120.15	Good
Case5	Female	26	38 200	8111	109.66	Good
Case6	Female	27	38 995	10 538	103.67	Excellent
Case7	Female	54	45 045	11 346	105.87	Excellent
Case8	Female	32	38 480	18 646	87.22	Excellent
Case9	Female	33	37 370	17 929	88.02	Excellent
Case10	Female	35	35 070	15 663	134.82	Good
Case11	Female	36	34 120	11 825	147.26	Excellent
Case12	Female	63	45 875	12 569	83.74	Moderate
Case13	Female	64	45 250	13 219	84.30	Good
Case14	Female	38	39 495	9911	128.67	Good
Case15	Female	39	38 105	7527	125.40	Good
Case16	Female	41	38 240	6539	112.07	Excellent
Case17	Female	42	36 895	3917	116.50	Good
Case18	Male	33	43 880	11 475	130.13	Excellent
Case19	Male	34	43 725	10 750	122.36	Excellent
Case20	Male	26	39 115	10 094	104.42	Excellent
Case21	Male	27	37 945	11 575	123.99	Excellent

图11 算法规划路径与医师规划路径对比图

Fig.11 Comparison between algorithmically planned paths and doctor-planned paths.

表8 算法与医师规划路径的对比结果

Tab.8 Comparison of the plans by the algorithm and the physician (Mean±SD)

Soft constraints	Algorithm (ours)	Physician	t/Z	P
D_iliac(mm)	9.44±5.10	11.88±6.14	2.381	0.017
D_Dura(mm)	17.44±2.38	17.23±2.55	2.212	0.039
D_L5(mm)	8.12±1.66	7.20±1.79	3.875	<0.001
D_N5(mm)	6.13±1.84	5.66±1.83	3.980	<0.001
D_S1(mm)	0.58±0.17	1.25±0.18	12.727	<0.001
Depth(mm)	113.92±17.44	119.51±18.60	7.008	<0.001
A_Kambin(mm²)	170.87±59.87	148.70±72.83	2.868	0.004

表9 自动分割和手动分割数据的算法性能比较

Tab.9 Algorithm performance comparison of auto-segmented and manually segmented data (Mean±SD)

Soft constraints	Auto segmentation	Manual segmentation	t/Z	P
D_iliac(mm)	7.54±5.54	9.44±5.10	2.958	0.008
D_Dura(mm)	18.43±2.18	17.44±2.38	2.071	0.052
D_L5(mm)	7.99±1.49	8.12±1.66	0.535	0.599
D_N5(mm)	5.94±1.92	6.13±1.84	0.678	0.498
D_S1(mm)	1.07±0.94	0.58±0.17	2.311	0.021
Depth(mm)	111.90±16.97	113.92±17.44	1.477	0.140
A_Kambin(mm²)	154.62±48.07	170.87±59.87	2.066	0.052

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面向L5/S1微创椎间盘射频消融术的多约束最优穿刺路径规划算法

A multi-constraint optimal puncture path planning algorithm for percutaneous interventional radiofrequency thermal fusion of the L5/S1 segments

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