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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(4): 1124-1133
Longwei Fang, Zuowei Wang, Zhiqiang Chen, Fengzeng Jian, Shuo Li and Huiguang He, "3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1124-1133, July 2020. doi: 10.1109/JAS.2019.1911528
Citation: Longwei Fang, Zuowei Wang, Zhiqiang Chen, Fengzeng Jian, Shuo Li and Huiguang He, "3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1124-1133, July 2020. doi: 10.1109/JAS.2019.1911528
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3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

doi: 10.1109/JAS.2019.1911528
Funds:  This work was supported in part by The National Key Research and Development Program of China (2018YFC2001302), the National Natural Science Foundation of China (61976209), CAS International Collaboration Key Project (173211KYSB20190024), and Strategic Priority Research Program of CAS (XDB32040000)
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    Abstract
  • Structure reconstruction of 3D anatomy from bi-planar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh. Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field. Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and 1.2 mm, separately. The average reconstruction time is 3 minutes.

     

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    References(36)
  • [1]
    Y. Allam, J. Silbermann, F. Riese, and R. Greiner-Perth, “Computer tomography assessment of pedicle screw placement in thoracic spine: Comparison between free hand and a generic 3D-based navigation techniques,” European Spine J., vol. 22, no. 3, pp. 648–653, 2013. doi: 10.1007/s00586-012-2505-7
    [2]
    G. Cui, Y. Wang, T.-H. Kao, Y. Zhang, Z. Liu, B. Liu, J. Li, X. Zhang, S. Zhu, N. Lu, et al., “Application of intraoperative computed tomography with or without navigation system in surgical correction of spinal deformity: A preliminary result of 59 consecutive human cases,” Spine, vol. 37, no. 10, pp. 891–900, 2012. doi: 10.1097/BRS.0b013e31823aff81
    [3]
    E. Van de Kelft, F. Costa, D. Van der Planken, and F. Schils, “A prospective multicenter registry on the accuracy of pedicle screw placement in the thoracic, lumbar, and sacral levels with the use of the o-arm imaging system and stealthstation navigation,” Spine, vol. 37, no. 25, pp. E1580–E1587, 2012. doi: 10.1097/BRS.0b013e318271b1fa
    [4]
    S. W. Tohtz, P. Rogalla, M. Taupitz, C. Perka, T. Winkler, and M. Putzier, “Inter-and intraobserver variability in the postoperative evaluation of transpedicular stabilization: Computed tomography versus magnetic resonance imaging,” The Spine Journal, vol. 10, no. 4, pp. 285–290, 2010. doi: 10.1016/j.spinee.2009.12.020
    [5]
    V. Amato, L. Giannachi, C. Irace, and C. Corona, “Accuracy of pedicle screw placement in the lumbosacral spine using conventional technique: Computed tomography postoperative assessment in 102 consecutive patients,” J. Neurosurgery:Spine, vol. 12, no. 3, pp. 306–313, 2010. doi: 10.3171/2009.9.SPINE09261
    [6]
    V. Pomero, D. Mitton, S. Laporte, J. A. de Guise, and W. Skalli, “Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric and statistic model,” Clinical Biomechanics, vol. 19, no. 3, pp. 240–247, 2004. doi: 10.1016/j.clinbiomech.2003.11.014
    [7]
    S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, and J. de Guise, “3D/2D registration and segmentation of scoliotic vertebrae using statistical models,” Computerized Medical Imaging and Graphics, vol. 27, no. 5, pp. 321–337, 2003. doi: 10.1016/S0895-6111(03)00019-3
    [8]
    J. Boisvert, F. Cheriet, X. Pennec, H. Labelle, N. Ayache, et al., “Articulated spine models for 3-D reconstruction from partial radiographic data,” IEEE Trans. Biomedical Engineering, vol. 55, no. 11, pp. 2565–2574, 2008. doi: 10.1109/TBME.2008.2001125
    [9]
    S. Kadoury, F. Cheriet, and H. Labelle, “Personalized X-ray 3-D reconstruction of the scoliotic spine from hybrid statistical and image-based models,” IEEE Trans. Medical Imaging, vol. 28, no. 9, pp. 1422–1435, 2009. doi: 10.1109/TMI.2009.2016756
    [10]
    S. Kolta, A. Le Bras, D. Mitton, V. Bousson, J. A. de Guise, J. Fechtenbaum, J. Laredo, C. Roux, and W. Skalli, “Three-dimensional X-ray absorptiometry (3D-XA): A method for reconstruction of human bones using a dual X-ray absorptiometry device,” Osteoporosis International, vol. 16, no. 8, pp. 969–976, 2005. doi: 10.1007/s00198-004-1782-3
    [11]
    A. Mitulescu, I. Semaan, J. A. De Guise, P. Leborgne, C. Adamsbaum, and W. Skalli, “Validation of the non-stereo corresponding points stereoradiographic 3D reconstruction technique,” Medical and Biological Engineering and Computing, vol. 39, no. 2, pp. 152–158, 2001. doi: 10.1007/BF02344797
    [12]
    D. Mitton, C. Landry, S. Veron, W. Skalli, F. Lavaste, and J. A. De Guise, “3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes,” Medical and Biological Engineering and Computing, vol. 38, no. 2, pp. 133–139, 2000. doi: 10.1007/BF02344767
    [13]
    S. Laporte, W. Skalli, J. De Guise, F. Lavaste, and D. Mitton, “A biplanar reconstruction method based on 2D and 3D contours: Application to the distal femur,” Computer Methods in Biomechanics&Biomedical Engineering, vol. 6, no. 1, pp. 1–6, 2003.
    [14]
    D. Mitton, S. Deschenes, S. Laporte, B. Godbout, S. Bertrand, J. A. de Guise, and W. Skalli, “3D reconstruction of the pelvis from biplanar radiography,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 9, no. 1, pp. 1–5, 2006. doi: 10.1080/10255840500521786
    [15]
    S. Benameur, M. Mignotte, H. Labelle, and J. A. De Guise, “A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine,” IEEE Trans. Biomedical Engineering, vol. 52, no. 12, pp. 2041–2057, 2005. doi: 10.1109/TBME.2005.857665
    [16]
    G. Zheng, X. Dong, K. T. Rajamani, X. Zhang, M. Styner, R. U. Thoranaghatte, L.-P. Nolte, and M. A. G. Ballester, “Accurate and robust reconstruction of a surface model of the proximal femur from sparsepoint data and a dense-point distribution model for surgical navigation,” IEEE Trans. Biomedical Engineering, vol. 54, no. 12, pp. 2109–2122, 2007. doi: 10.1109/TBME.2007.895736
    [17]
    M. Fleute, S. Lavallee, and R. Julliard, “Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery,” Medical Image Analysis, vol. 3, no. 3, pp. 209–222, 1999. doi: 10.1016/S1361-8415(99)80020-6
    [18]
    Z. Zhu and G. Li, “Construction of 3D human distal femoral surface models using a 3D statistical deformable model,” J. Biomechanics, vol. 44, no. 13, pp. 2362–2368, 2011. doi: 10.1016/j.jbiomech.2011.07.006
    [19]
    N. Baka, B. L. Kaptein, M. de Bruijne, T. van Walsum, J. Giphart, W. J. Niessen, and B. P. Lelieveldt, “2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models,” Medical Image Analysis, vol. 15, no. 6, pp. 840–850, 2011. doi: 10.1016/j.media.2011.04.001
    [20]
    T. Whitmarsh, L. Humbert, L. M. D. R. Barquero, S. Di Gregorio, and A. F. Frangi, “3D reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy X-ray absorptiometry,” Medical Image Analysis, vol. 17, no. 4, pp. 475–487, 2013. doi: 10.1016/j.media.2013.02.002
    [21]
    J. Yao and R. Taylor, “Deformable 2D-3D medical image registration using a statistical model: Accuracy factor assessment,” American J. Science and Engineering, vol. 1, no. 2, pp. 1–13, 2012.
    [22]
    A. Hurvitz and L. Joskowicz, “Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences,” Int. J. Computer Assisted Radiology and Surgery, vol. 3, no. 6, pp. 493–504, 2008. doi: 10.1007/s11548-008-0264-z
    [23]
    Z. Purisha, S. S. Karhula, J. H. Ketola, J. Rimpeläinen, M. T. Nieminen, S. Saarakkala, H. Kröger, and S. Siltanen, “An automatic regularization method: An application for 3-D X-ray micro-CT reconstruction using sparse data,” IEEE Trans. Medical Imaging, vol. 38, no. 2, pp. 417–425, 2019. doi: 10.1109/TMI.2018.2865646
    [24]
    A. M. Vukicevic, S. Çimen, N. Jagic, G. Jovicic, A. F. Frangi, and N. Filipovic, “Three-dimensional reconstruction and nurbs-based structured meshing of coronary arteries from the conventional X-ray angiography projection images,” Scientific Reports, vol. 8, no. 1, pp. 1711, 2018. doi: 10.1038/s41598-018-19440-9
    [25]
    W. A. Barrett and E. N. Mortensen, “Interactive live-wire boundary extraction,” Medical Image Analysis, vol. 1, no. 4, pp. 331–341, 1997. doi: 10.1016/S1361-8415(97)85005-0
    [26]
    N. Ohtsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Systems Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. doi: 10.1109/TSMC.1979.4310076
    [27]
    J. Meyer-Spradow, T. Ropinski, J. Mensmann, and K. Hinrichs, “Voreen: A rapid-prototyping environment for ray-casting-based volume visualizations,” IEEE Computer Graphics and Applications, vol. 29, no. 6, pp. 6–13, 2009. doi: 10.1109/MCG.2009.130
    [28]
    S. Du, Y. Guo, G. Sanroma, D. Ni, G. Wu, and D. Shen, “Building dynamic population graph for accurate correspondence detection,” Medical Image Analysis, vol. 26, no. 1, pp. 256–267, 2015. doi: 10.1016/j.media.2015.10.001
    [29]
    S. Du, J. Liu, B. Bi, J. Zhu, and J. Xue, “New iterative closest point algorithm for isotropic scaling registration of point sets with noise,” J. Visual Communication and Image Representation, vol. 38, pp. 207–216, 2016. doi: 10.1016/j.jvcir.2016.02.019
    [30]
    D. Shaoyi, X. Guanglin, Z. Sirui, Z. Xuetao, G. Yue, and C. Badong, “Robust rigid registration algorithm based on pointwise correspondence and correntropy,” Pattern Recognition Letters, 2018. doi: 10.1016/j.patrec.2018.06.028
    [31]
    G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V. Dalca, “An unsupervised learning model for deformable medical image registration,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 9252–9260, 2018.
    [32]
    D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, “Pet-CT image registration in the chest using free-form deformations,” IEEE Trans. Medical Imaging, vol. 22, no. 1, pp. 120–128, 2003. doi: 10.1109/TMI.2003.809072
    [33]
    J. Yao, J. E. Burns, H. Munoz, and R. M. Summers, “Detection of vertebral body fractures based on cortical shell unwrapping,” in Proc. Int. Conf. Medical Image Computing and Computer Assisted Intervention, pp. 509–516, 2012.
    [34]
    L. Humbert, J. A. De Guise, B. Aubert, B. Godbout, and W. Skalli, “3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences,” Medical Engineering&Physics, vol. 31, no. 6, pp. 681–687, 2009.
    [35]
    D. C. Moura, J. Boisvert, J. G. Barbosa, H. Labelle, and J. M. R. Tavares, “Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model,” Medical Engineering&Physics, vol. 33, no. 8, pp. 924–933, 2011.
    [36]
    S. Prakoonwit, “Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications,” Int. J. Art,Culture and Design Technologies(IJACDT), vol. 4, no. 1, pp. 13–31, 2014. doi: 10.4018/IJACDT
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    Highlights

    • This paper introduces a novel method that use prior model and two x-ray images to reconstruct 3D vertebra.
    • We use the CT data of a vertebra specimen to provide both the shape mesh and the intensity model, and only one prior model used in our method.
    • We combine the elastic-mesh-based and statistical-intensity-model-based methods, which can provide efficient and robust 3D vertebra reconstruction.

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