Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model

  • Authors:

  • Guoyan Zheng;Miguel Á.G. Ballester;Martin Styner;Lutz-Peter Nolte

  • Affiliations:

  • MEM Research Center, University of Bern, Bern, Switzerland;MEM Research Center, University of Bern, Bern, Switzerland;Departments of Computer Science and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC;MEM Research Center, University of Bern, Bern, Switzerland

  • Venue:
  • MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
  • Year:
  • 2006

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Abstract

Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and a point distribution model is discussed. We present a 2D/3D reconstruction scheme combining statistical extrapolation and regularized shape deformation with an iterative image-to-model correspondence establishing algorithm, and show its application to reconstruct the surface of proximal femur. The image-to-model correspondence is established using a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformation to find a fraction of best matched 2D point pairs between features detected from the fluoroscopic images and those extracted from the 3D model. The obtained 2D 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. We designed and conducted experiments on 11 cadaveric femurs to validate the present reconstruction scheme. An average mean reconstruction error of 1.2 mm was found when two fluoroscopic images were used for each bone. It decreased to 1.0 mm when three fluoroscopic images were used.