Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness

Citation:

Olivier Clatz, Hervé Delingette, Ion-Florin Talos, Alexandra J Golby, Ron Kikinis, Ferenc A Jolesz, Nicholas Ayache, and Simon K Warfield. 2005. “Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness.” Med Image Comput Comput Assist Interv, 8, Pt 2, Pp. 295-302.

Abstract:

We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.
Last updated on 01/24/2017