Patient-Specific 3D Reconstruction of a Complete Lower Extremity from 2D X-rays

Introduction In clinical routine surgeons depend largely on 2D x-ray radiographs and their experience to plan and evaluate surgical interventions around the knee joint. Numerous studies have shown that pure 2D x-ray radiography based measurements are not accurate due to the error in determining accurate radiography magnification and the projection characteristics of 2D radiographs. Using 2D x-ray radiographs to plan 3D knee joint surgery may lead to component misalignment in Total Knee Arthroplasty (TKA) or to over- or under-correction of the mechanical axis in Lower Extremity Osteotomy (LEO). Recently we developed a personalized X-ray reconstruction-based planning and post-operative treatment evaluation system called “iLeg” for TKA or LEO. Based on a patented X-ray image calibration cage and a unique 2D–3D reconstruction technique, iLeg can generate accurate patient-specific 3D models of a complete lower extremity from two standing X-rays for true 3D planning and evaluation of surgical interventions at the knee joint. The goal of this study is to validate the accuracy of this newly developed system using digitally reconstructed radiographs (DRRs) generated from CT data of cadavers. Methods CT data of 12 cadavers (24 legs) were used in the study. For each leg, two DRRs, one from the antero-posterior (AP) direction and the other from the later-medial (LM) direction, were generated following clinical requirements and used as the input to the iLeg software. The 2D–3D reconstruction was then done by non-rigidly matching statistical shape models (SSMs) of both femur and tibia to the DRRs (seee Fig. 1). In order to evaluate the 2D–3D reconstruction accuracy, we conducted a semi-automatic segmentation of all CT data using the commercial software Amira (FEI Corporate, Oregon, USA). The reconstructed surface models of each leg were then compared with the surface models segmented from the associated CT data. Since the DRRs were generated from the associated CT data, the surface models were reconstructed in the local coordinate system of the CT data. Thus, we can directly compare the reconstructed surface models with the surface models segmented from the associated CT data, which we took as the ground truth. Again, we used the software Amira to compute distances from each vertex on the reconstructed surface models to the associated ground truth models. Results When the reconstructed models were compared with the surface models segmented from the associated CT data, a mean reconstruction accuracy of 1.2±0.2mm, 1.3±0.2mm, 1.4±0.3mm and 1.3±0.2mm was found for left femur, right femur, left tibia and right tibia, respectively. When looking into the reconstruction of each subject, we found an average reconstruction accuracy in the range of 1.1mm to 1.5mm. Overall, the reconstruction accuracy was found to be 1.3±0.2mm. Discussions We presented a cadaver study to validate the accuracy of reconstructing 3D patient-specific models of a complete lower extremity from 2D X-rays. Our experimental results demonstrate that the complete lower extremity can be reconstructed accurately from 2D X-rays. Please note that the errors we reported above include both pose and shape reconstruction errors whole most of previous studies only reported shape reconstruction errors.

[1]  Guoyan Zheng,et al.  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 , 2009, Medical Image Anal..

[2]  Yvan Petit,et al.  Three-dimensional (3-D) reconstruction of the spine from a single X-ray image and prior vertebra models , 2004, IEEE Transactions on Biomedical Engineering.

[3]  L P Nolte,et al.  Fluoroscopy as an imaging means for computer-assisted surgical navigation. , 1999, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[4]  Guoyan Zheng,et al.  Personalized X-ray Reconstruction of the Proximal Femur via a New Control Point-based 2D-3D Registration and Residual Complexity Minimization , 2014, VCBM.

[5]  M. Wybier,et al.  Musculoskeletal imaging in progress: the EOS imaging system. , 2013, Joint, bone, spine : revue du rhumatisme.

[6]  Farida Cheriet,et al.  A Novel System for the 3-D Reconstruction of the Human Spine and Rib Cage From Biplanar X-Ray Images , 2007, IEEE Transactions on Biomedical Engineering.

[7]  Guoyan Zheng,et al.  Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph , 2010, Annals of Biomedical Engineering.

[8]  Guoyan Zheng,et al.  Calibration of C-arm for orthopedic interventions via statistical model-based distortion correction and robust phantom detection , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[9]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[10]  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..

[11]  P. Newton,et al.  Comparison of 3-Dimensional Spinal Reconstruction Accuracy: Biplanar Radiographs With EOS Versus Computed Tomography , 2012, Spine.

[12]  Leo Joskowicz,et al.  Fluroscopic Image Processing for Computer-Aided Orthopaedic Surgery , 1998, MICCAI.

[13]  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.

[14]  Omar Ahmad,et al.  Volumetric DXA (VXA): A new method to extract 3D information from multiple in vivo DXA images , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[15]  Guoyan Zheng Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration , 2011, MICCAI.

[16]  Russell H. Taylor,et al.  Deformable 2D-3D Registration of the Pelvis with a Limited Field of View, Using Shape Statistics , 2007, MICCAI.

[17]  Gabor Fichtinger,et al.  C-arm Tracking and Reconstruction Without an External Tracker , 2006, MICCAI.

[18]  T.M. Buzug,et al.  Registration Algorithm for Statistical Bone Shape Reconstruction from Radiographs - An Accuracy Study , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Leo Joskowicz,et al.  Robust Automatic C-Arm Calibration for Fluoroscopy-Based Navigation: A Practical Approach , 2002, MICCAI.