Abstract
The use of preoperative CT and intraoperative fluoroscopic-guided surgical robotic assistance for spinal disease treatment has gained significant attention among surgeons. However, the intraoperative robotic systems lack guidance based on three-dimensional anatomical structures, rendering them unusable when there is a mismatch between preoperative CT and intraoperative fluoroscopy. Additionally, the continuous X-ray imaging during fluoroscopy exposes the patient to significant radiation risks. This paper proposes a 2D/3D registration method to reconstruct 3D patient spine geometry from frontal-view X-ray images. Our aim is to combine this method with intraoperative robotic assistance for spine procedures, enabling three-dimensional spatial navigation for the robotic system and reducing patient radiation exposure. By utilizing 45 frontal-view X-ray images of patients with scoliosis, we trained a 3D trunk anatomy SSM specific to scoliosis. For the registration, a 2D registration-back projection strategy was employed, which first generates the simulated X-ray projection image of the 3D SSM, then non-rigidly registers the projection image with the patient X-ray image, and finally back-project the 2D registration into 3D to guide the shape deformation of the 3D SSM. This approach was evaluated using X-ray images of 10 scoliosis patients. The results demonstrate the ability to reconstruct the anatomical structure and curvature of the spine in X-ray images. The registration accuracy of the SSM was assessed by visualizing the alignment between the registered model and X-ray images, as well as calculating the average distance between the vertebral disc center points of the model and expert-annotated anatomical landmarks in the patient’s X-rays. The average distance between the registered model’s vertebral disc centers and the patient’s X-ray anatomical positions was 11.38 mm. The registered model exhibited a high degree of alignment with the curvature of the spine in X-ray images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cheng, J., Castelein, R., Chu, W., Danielsson, A.: Adolescent idiopathic scoliosis. Nat. Rev. Dis. Primers 1(1), 15030 (2015)
Hu, X., Ohnmeiss, D.D., Lieberman, I.H.: Robotic-assisted pedicle screw placement: lessons learned from the first 102 patients. Eur. Spine J. 22(3), 661–666 (2013)
Fan, Y., et al.: Accuracy of pedicle screw placement comparing robot-assisted technology and the free-hand with fluoroscopy-guided method in spine surgery: an updated meta-analysis. Medicine (Baltimore) 97(22), e10970 (2018)
Ueno, J., et al.: Robotics is useful for less-experienced surgeons in spinal deformity surgery. Eur. J. Orthop. Surg. Traumatol. 1–6 (2022)
Macke, J.J., Woo, R., Varich, L.: Accuracy of robot-assisted pedicle screw placement for adolescent idiopathic scoliosis in the pediatric population. J. Robot. Surg. 10(2), 145–150 (2016)
Akazawa, T., et al.: Accuracy of computer-assisted pedicle screw placement for adolescent idiopathic scoliosis: a comparison between robotics and navigation. Eur. Spine J. 32(2), 651–658 (2023)
Hyun, S.J., Kim, K.J., Jahng, T.A., Kim, H.J.: Minimally invasive robotic versus open fluoroscopic-guided spinal instrumented fusions: a randomized controlled trial. Spine 42(6), 353–358 (2017)
Goswami, B., Misra, S.K.: 3D modeling of X-ray images: a review. Int. J. Comput. Appl. 132, 40–46 (2015)
Reyneke, C.J.F., Luthi, M., Burdin, V., Douglas, T.S., Vetter, T., Mutsvangwa, T.E.M.: Review of 2-D/3-D reconstruction using statistical shape and intensity models and X-ray image synthesis: toward a unified framework. IEEE Rev. Biomed. Eng. 12, 269–286 (2019)
Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)
Dworzak, J., et al.: 3D reconstruction of the human rib cage from 2D projection images using a statistical shape model. Int. J. Comput. Assist. Radiol. Surg. 5(2), 111–124 (2010)
Sadowsky, O., Lee, J., Sutter, E.G., Wall, S.J., Prince, J.L., Taylor, R.H.: Hybrid cone-beam tomographic reconstruction: incorporation of prior anatomical models to compensate for missing data. IEEE Trans. Med. Imaging 30(1), 69–83 (2011)
Zheng, G., Gollmer, S., Schumann, S., Dong, X., Feilkas, T., González Ballester, M.A.: A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images. Med. Image Anal. 13(6), 883–899 (2009)
Baka, N., et al.: 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med. Image Anal. 15(6), 840–850 (2011)
Wang, H., Stout, D.B., Chatziioannou, A.F.: Mouse atlas registration with non-tomographic imaging modalities – a pilot study based on simulation. Mol. Imag. Biol. 14(4), 408–419 (2012)
Wang, H., et al.: MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera. Phys. Med. Biol. 57(19), 6063–6077 (2012)
Wang, H., Stout, D.B., Chatziioannou, A.F.: A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo. Med. Image Anal. 17(4), 401–416 (2013)
Novosad, J., Cheriet, F., Petit, Y., Labelle, H.: Three-dimensional (3-D) reconstruction of the spine from a single X-ray image and prior vertebra models. IEEE Trans. Biomed. Eng. 51(9), 1628–1639 (2004)
Zhang, J., et al.: 3-D reconstruction of the spine from biplanar radiographs based on contour matching using the Hough transform. IEEE Trans. Biomed. Eng. 60(7), 1954–1964 (2013)
Sun, X., et al.: A statistical model of spine shape and material for population-oriented biomechanical simulation. IEEE Access 9, 155805–155814 (2021)
Bookstein, F.L.: Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med. Image Anal. 1(3), 225–243 (1997)
Wang, H., Yu, D., Tan, Z., Hu, R., Zhang, B., Yu, J.: Estimation of thyroid volume from scintigraphy through 2D/3D registration of a statistical shape model. Phys. Med. Biol. 64(9), 095015 (2019)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010)
Acknowledgements
This work was supported in part by the National Key Research and Development Program No. 2020YFB1711500, 2020YFB1711501, 2020YFB1711503, the general program of the National Natural Science Fund of China (No. 81971693, 61971445), the funding of Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Hainan Province Key Research and Development Plan ZDYF2021SHFZ244, the Fundamental Research Funds for the Central Universities Fundamental Research Funds for the Central Universities (No. DUT22YG229 and DUT22YG205), the funding of Liaoning Key Lab of IC & BME System and the 1-3-5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYYC21004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, Y. et al. (2023). 2D/3D Shape Model Registration with X-ray Images for Patient-Specific Spine Geometry Reconstruction. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14272. Springer, Singapore. https://doi.org/10.1007/978-981-99-6480-2_46
Download citation
DOI: https://doi.org/10.1007/978-981-99-6480-2_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6479-6
Online ISBN: 978-981-99-6480-2
eBook Packages: Computer ScienceComputer Science (R0)