3D reconstruction of highly fragmented bone fractures

A system for the semi-automatic reconstruction of highly fragmented bone fractures, developed to aid in treatment planning, is presented. The system aligns bone fragment surfaces derived from segmentation of volumetric CT scan data. Each fragment surface is partitioned into intact- and fracture-surfaces, corresponding more or less to cortical and cancellous bone, respectively. A user then interactively selects fracture-surface patches in pairs that coarsely correspond. A final optimization step is performed automatically to solve the N-body rigid alignment problem. The work represents the first example of a 3D bone fracture reconstruction system and addresses two new problems unique to the reconstruction of fractured bones: (1) non-stationary noise inherent in surfaces generated from a difficult segmentation problem and (2) the possibility that a single fracture surface on a fragment may correspond to many other fragments.

[1]  Thomas S. Huang,et al.  Image processing , 1971 .

[2]  Sing Bing Kang,et al.  Registration and integration of textured 3-D data , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[3]  J L Marsh,et al.  Use of an articulated external fixator for fractures of the tibial plafond. , 1995, The Journal of bone and joint surgery. American volume.

[4]  Thomas D Brown,et al.  Posttraumatic Osteoarthritis: A First Estimate of Incidence, Prevalence, and Burden of Disease , 2006, Journal of orthopaedic trauma.

[5]  Thomas D Brown,et al.  Interfragmentary surface area as an index of comminution energy: proof of concept in a bone fracture surrogate. , 2002, Journal of biomechanics.

[6]  Andrew E. Johnson,et al.  Registration and integration of textured 3-D data , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[7]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..

[8]  S. Furner,et al.  Musculoskeletal Conditions in the United States , 1992 .

[9]  Marc Rioux,et al.  Three-dimensional registration using range and intensity information , 1994, Other Conferences.

[10]  Kari Pulli,et al.  Multiview registration for large data sets , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[11]  H. Pottmann,et al.  Reassembling fractured objects by geometric matching , 2006, SIGGRAPH 2006.

[12]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[13]  Xiaodong Wu,et al.  Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Georgios Papaioannou,et al.  Reconstruction of Three-Dimensional Objects through Matching of Their Parts , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Leo Joskowicz,et al.  Computer-Based Periaxial Rotation Measurement for Aligning Fractured Femur Fragments: Method and Preliminary Results , 2001, MICCAI.

[16]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  David G. Stork,et al.  Pattern Classification , 1973 .

[18]  Dongjin Ha Bayesian pot-assembly from fragments as problems in perceptual-grouping and geometric-learning , 2002, Object recognition supported by user interaction for service robots.

[19]  J Lawrence Marsh,et al.  Tibial Plafond Fractures: How Do These Ankles Function Over Time? , 2003, The Journal of bone and joint surgery. American volume.