X-ray-guided lung puncture navigation integrating direct linear transformation-Gauss-Newton method and marker-free registration_Journal of Biomedical Engineering

Authors：

QIAN Yuanyuan ¹ , LI Maochen ^2,3 , ZHANG Chen ¹ , HUANG Duo ⁴ , JI Xuquan ³ ,  LUO Xiaobo ⁴

1. Department of Thoracic Surgery, Suzhou BenQ Hospital, Suzhou, Jiangsu 215000, P. R. China;
2. School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. China;
3. Suzhou Zhuzheng Robot Co., Ltd., Suzhou, Jiangsu 215000, P. R. China;
4. Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing 100089, P. R. China;

Corresponding?author：

LUO Xiaobo, Email: Luoxiaobo_309@163.com

Keywords：

Percutaneous transthoracic needle biopsy; X-ray navigation; Direct linear transformation-Gauss-Newton method; Marker-free registration; Respiratory phase control

DOI：

10.7507/1001-5515.202606035

Video：

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Abstract Full text Figures/Tables Video References Cited by

Percutaneous transthoracic needle biopsy (PTNB) is an important modality for the qualitative diagnosis of lung cancer, which relies on medical-image guidance. X-ray equipment features wide availability and low radiation dose; nevertheless, X-ray-guided PTNB requires repeated calibration using physical calibrators. Accordingly, this paper proposes an X-ray-guided lung puncture navigation method integrating the direct linear transformation-Gauss-Newton method (DLT-GNM) and marker-free registration. Preoperatively, initial imaging parameters are acquired via fluoroscopic calibration. Intraoperatively, a binocular camera tracks the spatial pose of the C-arm in real time to virtually reconstruct calibration points. DLT-GNM is employed to solve the projection model and realize marker-free real-time navigation. Respiratory phantom experiments show that the overall localization error is (0.38 ± 0.25) mm under matched respiratory phases. It is verified that this method delivers high accuracy and simple operation, and possesses favorable clinical application potential for cases with large lesions or identifiable targets clearly visualized under X-ray fluoroscopy.

Citation： QIAN Yuanyuan, LI Maochen, ZHANG Chen, HUANG Duo, JI Xuquan, LUO Xiaobo. X-ray-guided lung puncture navigation integrating direct linear transformation-Gauss-Newton method and marker-free registration. Journal of Biomedical Engineering, 2026, 43(5): 1026-1034. doi: 10.7507/1001-5515.202606035 Copy

1.	張輝標, 唐東方, 高文. 納米藥物在肺癌免疫治療中的研究進展[J]. 老年醫學與保健, 2023, 29(5): 1094-1100.
2.	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
3.	Siegel R L, Miller K D, Jemal A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(1): 7-30.
4.	Lee K H, Lim K Y, Suh Y J, et al. Nondiagnostic percutaneous transthoracic needle biopsy of lung lesions: a multicenter study of malignancy risk[J]. Radiology, 2019, 293(3): 627-635.
5.	Manhire A, Charig M, Clelland C, et al. Guidelines for radiologically guided lung biopsy[J]. Thorax, 2003, 58(11): 920-936.
6.	Baldwin D R, Callister M E, Guideline Development Group. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules[J]. Thorax, 2015, 70(8): 794-798.
7.	Gould M K, Donington J, Lynch W R, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines[J]. Chest, 2013, 143(5 Suppl): e93S-e120S.
8.	崔飛, 陳延偉, 薛興陽, 等. 穿刺手術機器人輔助經皮肺穿刺診療技術: 臨床規范化應用專家共識[J]. 中華胸部外科電子雜志, 2025, 12(1): 1-11.
9.	顧愛琴, 熊麗紋, 韓寶惠, 等. CT引導下經皮肺穿刺活檢術的臨床應用[J]. 中國癌癥雜志, 2005, 15(1): 90-91.
10.	Fontaine-Delaruelle C, Souquet P J, Gamondes D, et al. Negative predictive value of transthoracic core-needle biopsy: a multicenter study[J]. Chest, 2015, 148(2): 472-480.
11.	Yang K, Ganguli S, DeLorenzo M C, et al. Procedure-specific CT dose and utilization factors for CT-guided interventional procedures[J]. Radiology, 2018, 289(1): 150-157.
12.	Heerink W J, de Bock G H, de Jonge G J, et al. Complication rates of CT-guided transthoracic lung biopsy: meta-analysis[J]. Eur Radiol, 2017, 27(1): 138-148.
13.	Abi-Jaoudeh N, Fisher T, Jacobus J, et al. Prospective randomized trial for image guided biopsy using cone beam computed tomography navigation compared to conventional computed tomography[J]. J Vasc Interv Radiol, 2016, 27(9): 1342-1349.
14.	Hong W, Yoon S H, Goo J M, et al. Cone-beam CT-guided percutaneous transthoracic needle lung biopsy of juxtaphrenic lesions: diagnostic accuracy and complications[J]. Korean J Radiol, 2021, 22(7): 1203-1212.
15.	Oh H S, Han Y M, Choi K C, et al. Fluoroscopically guided automated gun biopsy of chest lesions: diagnostic accuracy and complications[J]. J Korean Radiol Soc, 1997, 36(6): 921-926.
16.	Ren Q, Zhou Y, Yan M, et al. Imaging-guided percutaneous transthoracic needle biopsy of nodules in the lung base: fluoroscopy CT versus cone-beam CT[J]. Clin Radiol, 2022, 77(5): e394-e399.
17.	Sidhu J S, Salte G, Christiansen I S, et al. Fluoroscopy guided percutaneous biopsy in combination with bronchoscopy and endobronchial ultrasound in the diagnosis of suspicious lung lesions: the triple approach[J]. Eur Clin Respir J, 2020, 7(1): 1723303.
18.	Miotto A, Perfeito J A J, Pacheco R L, et al. Minimally invasive interventions for biopsy of malignancy-suspected pulmonary nodules: a systematic review and meta-analysis[J]. Sao Paulo Med J, 2023, 141(5): e2022543.
19.	Cho S H, Yoon H J, Lee Y, et al. Fluoroscopy-guided percutaneous transthoracic needle lung biopsy with the aid of planning cone-beam CT: diagnostic accuracy and complications[J]. Diagnostics, 2024, 14(21): 2441.
20.	Li J, Su L, Liu J, et al. Optical navigation robot-assisted puncture system for accurate lung nodule biopsy: an animal study[J]. Quant Imaging Med Surg, 2023, 13(12): 7789-7801.
21.	Xia W, Wu Z, Zuo R, et al. Feasibility and accuracy evaluation of novel 2D instant navigation system on spinal surgery-a preclinical study[J]. BMC Musculoskelet Disord, 2025, 26(1): 510.
22.	Flepp R, Nieuwland A, Sigrist B, et al. Electromagnetic navigation for femoral osteotomy using high-accuracy X-ray-to-CT registration[J]. Int J CARS, 2026. DOI: 10.1007/s11548-026-03726-9.
23.	Gao C, Phalen H, Sefati S, et al. Fluoroscopic navigation for a surgical robotic system including a continuum manipulator[J]. IEEE Trans Biomed Eng, 2022, 69(1): 453-464.
24.	Liebmann F, von Atzigen M, Stütz D, et al. Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery[J]. Med Image Anal, 2024, 91: 103027.
25.	張志華. CT影像與X線影像對肺結核療程判定及診斷的對比分析[J]. 醫藥前沿, 2012, 2(9): 211-212.
26.	李華. 數字化X線攝影對診斷肺部小結節病灶的臨床價值探討[J]. 醫藥與保健, 2019, 27(4): 27.
27.	Guo N, Yang B, Wang Y, et al. New calibrator with points distributed conical helically for online calibration of C-arm[J]. Sensors, 2019, 19(9): 1989.
28.	Oh J, Park J O, Park S, et al. Image-based guidance system for intravascular microrobot: fiducial marker-based registration using biplanar fluoroscopic images and CTA images[C]//2015 15th International Conference on Control, Automation and Systems (ICCAS), Busan: IEEE, 2015: 919-922.
29.	Ha H G, Han G, Lee S, et al. Robot-patient registration for optical tracker-free robotic fracture reduction surgery[J]. Comput Methods Programs Biomed, 2023, 228: 107239.
30.	Thorhauer E D, Wukelic C, Lin W, et al. Validation of marker-based tracking with a biplanar fluoroscopy system optimized for the foot and ankle[J]. Med Eng Phys, 2025, 138(1): 104310.
31.	Zhang T, Ji X, Zhu Y, et al. Robotic positioning in percutaneous transforaminal endoscopic discectomy based on X-ray image and DLT algorithm[C]//2023 WRC Symposium on Advanced Robotics and Automation (WRC SARA), Beijing: IEEE, 2023: 27-32.
32.	Wang J, Ji X, Zhang X, et al. Real-time robust individual X point localization for stereoscopic tracking[J]. Pattern Recognit Lett, 2018, 112: 138-144.
33.	史亞濱. 不同主動呼吸控制系統在左側乳腺癌保乳術后放療中的應用[D]. 汕頭: 汕頭大學, 2023: 1-60.
34.	Lian M, Li Y, Gu X, et al. A retrospective respiratory gating system based on epipolar consistency conditions[J]. Mol Cell Biomech, 2020, 17(1): 41.
35.	Li H, Wang S, Hu J, et al. Fusing prediction and perception: adaptive Kalman filter-driven respiratory gating for MR surgical navigation[J]. Sensors, 2026, 26(2): 405.
36.	Gopalakrishnan V, Dey N, Golland P. Intraoperative 2D/3D image registration via differentiable X-ray rendering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, 2024: 11662-11672.
37.	Li D, Mao Y, Tu P, et al. A robotic system for transthoracic puncture of pulmonary nodules based on gated respiratory compensation[J]. Comput Methods Programs Biomed, 2024, 244: 107995.
38.	Wagner M G, Periyasamy S, Longhurst C, et al. Real-time respiratory motion compensated roadmaps for hepatic arterial interventions[J]. Med Phys, 2021, 48(10): 5661-5673.
39.	Varble N, Chen A, Sinha A, et al. Determination of error in 3D CT to 2D fluoroscopy image registration for endobronchial guidance[C]//Medical Image Computing and Computer Assisted Intervention (MICCAI), Strasbourg: Springer, 2021: 335-344.
40.	Duan X, He R, Zhao Q, et al. Unified admittance control for accurate puncture and respiration following based on disturbance observation and model predictive control[J]. IEEE Robot Autom Lett, 2025, 10(4): 3526-3533.

1. 張輝標, 唐東方, 高文. 納米藥物在肺癌免疫治療中的研究進展[J]. 老年醫學與保健, 2023, 29(5): 1094-1100.
2. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
3. Siegel R L, Miller K D, Jemal A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(1): 7-30.
4. Lee K H, Lim K Y, Suh Y J, et al. Nondiagnostic percutaneous transthoracic needle biopsy of lung lesions: a multicenter study of malignancy risk[J]. Radiology, 2019, 293(3): 627-635.
5. Manhire A, Charig M, Clelland C, et al. Guidelines for radiologically guided lung biopsy[J]. Thorax, 2003, 58(11): 920-936.
6. Baldwin D R, Callister M E, Guideline Development Group. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules[J]. Thorax, 2015, 70(8): 794-798.
7. Gould M K, Donington J, Lynch W R, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines[J]. Chest, 2013, 143(5 Suppl): e93S-e120S.
8. 崔飛, 陳延偉, 薛興陽, 等. 穿刺手術機器人輔助經皮肺穿刺診療技術: 臨床規范化應用專家共識[J]. 中華胸部外科電子雜志, 2025, 12(1): 1-11.
9. 顧愛琴, 熊麗紋, 韓寶惠, 等. CT引導下經皮肺穿刺活檢術的臨床應用[J]. 中國癌癥雜志, 2005, 15(1): 90-91.
10. Fontaine-Delaruelle C, Souquet P J, Gamondes D, et al. Negative predictive value of transthoracic core-needle biopsy: a multicenter study[J]. Chest, 2015, 148(2): 472-480.
11. Yang K, Ganguli S, DeLorenzo M C, et al. Procedure-specific CT dose and utilization factors for CT-guided interventional procedures[J]. Radiology, 2018, 289(1): 150-157.
12. Heerink W J, de Bock G H, de Jonge G J, et al. Complication rates of CT-guided transthoracic lung biopsy: meta-analysis[J]. Eur Radiol, 2017, 27(1): 138-148.
13. Abi-Jaoudeh N, Fisher T, Jacobus J, et al. Prospective randomized trial for image guided biopsy using cone beam computed tomography navigation compared to conventional computed tomography[J]. J Vasc Interv Radiol, 2016, 27(9): 1342-1349.
14. Hong W, Yoon S H, Goo J M, et al. Cone-beam CT-guided percutaneous transthoracic needle lung biopsy of juxtaphrenic lesions: diagnostic accuracy and complications[J]. Korean J Radiol, 2021, 22(7): 1203-1212.
15. Oh H S, Han Y M, Choi K C, et al. Fluoroscopically guided automated gun biopsy of chest lesions: diagnostic accuracy and complications[J]. J Korean Radiol Soc, 1997, 36(6): 921-926.
16. Ren Q, Zhou Y, Yan M, et al. Imaging-guided percutaneous transthoracic needle biopsy of nodules in the lung base: fluoroscopy CT versus cone-beam CT[J]. Clin Radiol, 2022, 77(5): e394-e399.
17. Sidhu J S, Salte G, Christiansen I S, et al. Fluoroscopy guided percutaneous biopsy in combination with bronchoscopy and endobronchial ultrasound in the diagnosis of suspicious lung lesions: the triple approach[J]. Eur Clin Respir J, 2020, 7(1): 1723303.
18. Miotto A, Perfeito J A J, Pacheco R L, et al. Minimally invasive interventions for biopsy of malignancy-suspected pulmonary nodules: a systematic review and meta-analysis[J]. Sao Paulo Med J, 2023, 141(5): e2022543.
19. Cho S H, Yoon H J, Lee Y, et al. Fluoroscopy-guided percutaneous transthoracic needle lung biopsy with the aid of planning cone-beam CT: diagnostic accuracy and complications[J]. Diagnostics, 2024, 14(21): 2441.
20. Li J, Su L, Liu J, et al. Optical navigation robot-assisted puncture system for accurate lung nodule biopsy: an animal study[J]. Quant Imaging Med Surg, 2023, 13(12): 7789-7801.
21. Xia W, Wu Z, Zuo R, et al. Feasibility and accuracy evaluation of novel 2D instant navigation system on spinal surgery-a preclinical study[J]. BMC Musculoskelet Disord, 2025, 26(1): 510.
22. Flepp R, Nieuwland A, Sigrist B, et al. Electromagnetic navigation for femoral osteotomy using high-accuracy X-ray-to-CT registration[J]. Int J CARS, 2026. DOI: 10.1007/s11548-026-03726-9.
23. Gao C, Phalen H, Sefati S, et al. Fluoroscopic navigation for a surgical robotic system including a continuum manipulator[J]. IEEE Trans Biomed Eng, 2022, 69(1): 453-464.
24. Liebmann F, von Atzigen M, Stütz D, et al. Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery[J]. Med Image Anal, 2024, 91: 103027.
25. 張志華. CT影像與X線影像對肺結核療程判定及診斷的對比分析[J]. 醫藥前沿, 2012, 2(9): 211-212.
26. 李華. 數字化X線攝影對診斷肺部小結節病灶的臨床價值探討[J]. 醫藥與保健, 2019, 27(4): 27.
27. Guo N, Yang B, Wang Y, et al. New calibrator with points distributed conical helically for online calibration of C-arm[J]. Sensors, 2019, 19(9): 1989.
28. Oh J, Park J O, Park S, et al. Image-based guidance system for intravascular microrobot: fiducial marker-based registration using biplanar fluoroscopic images and CTA images[C]//2015 15th International Conference on Control, Automation and Systems (ICCAS), Busan: IEEE, 2015: 919-922.
29. Ha H G, Han G, Lee S, et al. Robot-patient registration for optical tracker-free robotic fracture reduction surgery[J]. Comput Methods Programs Biomed, 2023, 228: 107239.
30. Thorhauer E D, Wukelic C, Lin W, et al. Validation of marker-based tracking with a biplanar fluoroscopy system optimized for the foot and ankle[J]. Med Eng Phys, 2025, 138(1): 104310.
31. Zhang T, Ji X, Zhu Y, et al. Robotic positioning in percutaneous transforaminal endoscopic discectomy based on X-ray image and DLT algorithm[C]//2023 WRC Symposium on Advanced Robotics and Automation (WRC SARA), Beijing: IEEE, 2023: 27-32.
32. Wang J, Ji X, Zhang X, et al. Real-time robust individual X point localization for stereoscopic tracking[J]. Pattern Recognit Lett, 2018, 112: 138-144.
33. 史亞濱. 不同主動呼吸控制系統在左側乳腺癌保乳術后放療中的應用[D]. 汕頭: 汕頭大學, 2023: 1-60.
34. Lian M, Li Y, Gu X, et al. A retrospective respiratory gating system based on epipolar consistency conditions[J]. Mol Cell Biomech, 2020, 17(1): 41.
35. Li H, Wang S, Hu J, et al. Fusing prediction and perception: adaptive Kalman filter-driven respiratory gating for MR surgical navigation[J]. Sensors, 2026, 26(2): 405.
36. Gopalakrishnan V, Dey N, Golland P. Intraoperative 2D/3D image registration via differentiable X-ray rendering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, 2024: 11662-11672.
37. Li D, Mao Y, Tu P, et al. A robotic system for transthoracic puncture of pulmonary nodules based on gated respiratory compensation[J]. Comput Methods Programs Biomed, 2024, 244: 107995.
38. Wagner M G, Periyasamy S, Longhurst C, et al. Real-time respiratory motion compensated roadmaps for hepatic arterial interventions[J]. Med Phys, 2021, 48(10): 5661-5673.
39. Varble N, Chen A, Sinha A, et al. Determination of error in 3D CT to 2D fluoroscopy image registration for endobronchial guidance[C]//Medical Image Computing and Computer Assisted Intervention (MICCAI), Strasbourg: Springer, 2021: 335-344.
40. Duan X, He R, Zhao Q, et al. Unified admittance control for accurate puncture and respiration following based on disturbance observation and model predictive control[J]. IEEE Robot Autom Lett, 2025, 10(4): 3526-3533.

Journal of Biomedical Engineering

X-ray-guided lung puncture navigation integrating direct linear transformation-Gauss-Newton method and marker-free registration

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