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.
Constructing a 3D bone surface model from a limited number of calibrated 2D X-ray images (e.g. 2) and a 3D point distribution model is a challenging task, especially, when we would like to construct a patient-specific surface model of a bone with pathology. One of the key steps for such a 2D/3D reconstruction is to establish correspondences between the 2D images and the 3D model. This paper presents a 2D/3D correspondence building method based on a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformations to find a fraction of best matched 2D point pairs between features extracted from the X-ray images and those extracted from the 3D model. The estimated point pairs are then used to set up a set of 3D point pairs such that we turn a 2D/3D reconstruction problem to a 3D/3D one, whose solutions are well studied. Incorporating this 2D/3D correspondence building method, a 2D/3D reconstruction scheme combining a statistical instantiation with a regularized shape deformation has been developed. Comprehensive experiments on clinical datasets and on images of cadaveric femurs with both non-pathologic and pathologic cases are designed and conducted to evaluate the performance of the 2D/3D correspondence building method as well as that of the 2D/3D reconstruction scheme. Quantitative and qualitative evaluation results are given, which demonstrate the validity of the present method and scheme.
Zheng
Guoyan
G
MEM Research Center, Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014 Bern, Switzerland. guoyan.zheng@ieee.org
Gollmer
Sebastian
S
Schumann
Steffen
S
Dong
Xiao
X
Feilkas
Thomas
T
González Ballester
Miguel A
MA
D000465:Algorithms; D002138:Calibration; D003198:Computer Simulation; D005269:Femur / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016008:Statistical Distributions; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000592:standards