Fast 3d spine reconstruction of postoperative patients using a multilevel statistical model

  • Authors:
  • Fabian Lecron;Jonathan Boisvert;Saïd Mahmoudi;Hubert Labelle;Mohammed Benjelloun

  • Affiliations:
  • Computer Science Dept., Faculty of Engineering, University of Mons, Belgium;Information and Communications Technologies, National Research Council, Canada;Computer Science Dept., Faculty of Engineering, University of Mons, Belgium;Sainte-Justine Hospital, Montréal, Canada;Computer Science Dept., Faculty of Engineering, University of Mons, Belgium

  • Venue:
  • MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
  • Year:
  • 2012

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Abstract

Severe cases of spinal deformities such as scoliosis are usually treated by a surgery where instrumentation (hooks, screws and rods) is installed to the spine to correct deformities. Even if the purpose is to obtain a normal spine curve, the result is often straighter than normal. In this paper, we propose a fast statistical reconstruction algorithm based on a general model which can deal with such instrumented spines. To this end, we present the concept of multilevel statistical model where the data are decomposed into a within-group and a between-group component. The reconstruction procedure is formulated as a second-order cone program which can be solved very fast (few tenths of a second). Reconstruction errors were evaluated on real patient data and results showed that multilevel modeling allows better 3D reconstruction than classical models.