3D shape reconstruction of lumbar vertebra from two X-ray images and a CT model

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 specimenʼ 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.

[1]  Zhonglin Zhu,et al.  Construction of 3D human distal femoral surface models using a 3D statistical deformable model. , 2011, Journal of biomechanics.

[2]  Timo Ropinski,et al.  Voreen: A Rapid-Prototyping Environment for Ray-Casting-Based Volume Visualizations , 2009, IEEE Computer Graphics and Applications.

[3]  Leo Joskowicz,et al.  Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences , 2008, International Journal of Computer Assisted Radiology and Surgery.

[4]  W. Skalli,et al.  Three-dimensional X-ray absorptiometry (3D-XA): a method for reconstruction of human bones using a dual X-ray absorptiometry device , 2005, Osteoporosis International.

[5]  W. Skalli,et al.  Validation of the non-stereo corresponding points stereoradiographic 3D reconstruction technique , 2001, Medical and Biological Engineering and Computing.

[6]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[7]  Max Mignotte,et al.  A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine , 2005, IEEE Transactions on Biomedical Engineering.

[8]  Max Mignotte,et al.  3D/2D registration and segmentation of scoliotic vertebrae using statistical models. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[9]  Alejandro F. Frangi,et al.  3D reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy X-ray absorptiometry , 2013, Medical Image Anal..

[10]  D Mitton,et al.  A Biplanar Reconstruction Method Based on 2D and 3D Contours: Application to the Distal Femur , 2003, Computer methods in biomechanics and biomedical engineering.

[11]  Simant Prakoonwit.  Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications , 2014, Int. J. Art Cult. Des. Technol..

[12]  Jianru Xue,et al.  New iterative closest point algorithm for isotropic scaling registration of point sets with noise , 2016, J. Vis. Commun. Image Represent..

[13]  Mert R. Sabuncu,et al.  An Unsupervised Learning Model for Deformable Medical Image Registration , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  William A. Barrett,et al.  Interactive live-wire boundary extraction , 1997, Medical Image Anal..

[15]  Nicholas Ayache,et al.  Articulated Spine Models for 3-D Reconstruction From Partial Radiographic Data , 2008, IEEE Transactions on Biomedical Engineering.

[16]  Hubert Labelle,et al.  Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model. , 2011, Medical engineering & physics.

[17]  Stéphane Lavallée,et al.  Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery , 1999, Medical Image Anal..

[18]  W Skalli,et al.  3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences. , 2009, Medical engineering & physics.

[19]  Jianhua Yao,et al.  Deformable 2 D-3 D Medical Image Registration Using a Statistical Model : Accuracy Factor Assessment , 2012 .

[20]  Marleen de Bruijne,et al.  2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models , 2011, Medical Image Anal..

[21]  C. Corona,et al.  Accuracy of pedicle screw placement in the lumbosacral spine using conventional technique: computed tomography postoperative assessment in 102 consecutive patients. , 2010, Journal of neurosurgery. Spine.

[22]  F. Costa,et al.  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 , 2012, Spine.

[23]  Farida Cheriet,et al.  Personalized X-Ray 3-D Reconstruction of the Scoliotic Spine From Hybrid Statistical and Image-Based Models , 2009, IEEE Transactions on Medical Imaging.

[24]  Carsten Perka,et al.  Inter- and intraobserver variability in the postoperative evaluation of transpedicular stabilization: computed tomography versus magnetic resonance imaging. , 2010, The spine journal : official journal of the North American Spine Society.

[25]  Dinggang Shen,et al.  Building dynamic population graph for accurate correspondence detection , 2015, Medical Image Anal..

[26]  Ronald M. Summers,et al.  Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping , 2012, MICCAI.

[27]  David Mitton,et al.  Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric and statistic model. , 2004, Clinical biomechanics.

[28]  Badong Chen,et al.  Robust rigid registration algorithm based on pointwise correspondence and correntropy , 2020, Pattern Recognit. Lett..

[29]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[30]  Yasser Allam,et al.  Computer tomography assessment of pedicle screw placement in thoracic spine: comparison between free hand and a generic 3D-based navigation techniques , 2013, European Spine Journal.

[31]  D Mitton,et al.  3D reconstruction of the pelvis from bi-planar radiography , 2006, Computer methods in biomechanics and biomedical engineering.

[32]  Nenad Filipovic,et al.  Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images , 2018, Scientific Reports.

[33]  David Mitton,et al.  3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes , 2000, Medical and Biological Engineering and Computing.

[34]  Martin Styner,et al.  Accurate and Robust Reconstruction of a Surface Model of the Proximal Femur From Sparse-Point Data and a Dense-Point Distribution Model for Surgical Navigation , 2007, IEEE Transactions on Biomedical Engineering.

[35]  Simo Saarakkala,et al.  An Automatic Regularization Method: An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data , 2017, IEEE Transactions on Medical Imaging.

[36]  Yan Wang,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 , 2012, Spine.