Dense deformation field estimation for brain intraoperative images registration

Craene, M. S. De; d'Aische, Aloys du Bois; Talos, Ion-Florin; Ferrant, Matthieu; Black, Peter McL.; Jólesz, Ferenc A.; Kikinis, Ron; Macq, Benoı̂t; Warfield, Simon K.

doi:10.1117/12.534064

Cited by 2 publications

(2 citation statements)

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“…We have developed classes containing objects such as a finite element transformation associated with a regularization term, a cost function composed of a similarity metric and a smoothness term. These classes have been written to match the ITK registration framework and works in 2D and in 3D (De Craene et al, 2003). Moreover a new design allowing different meshing strategies has been implemented using an abstract class and several concrete classes of mesh generation (2D and 3D regular meshes and 2D octree-based mesh).…”

Section: Methodsmentioning

confidence: 99%

Efficient multi-modal dense field non-rigid registration: alignment of histological and section images

d'Aische

Craene

Geets³

et al. 2005

Medical Image Analysis

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Section: Methodsmentioning

confidence: 99%

Efficient multi-modal dense field non-rigid registration: alignment of histological and section images

d'Aische

Craene

Geets³

et al. 2005

Medical Image Analysis

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“…The data scans are segmented using the 3D Slicer, a surgical simulation and navigation tool [10]. Finally, to refine the mapping between the internal structures of the prostate, we used a mutual information based [16,13] non-rigid registration method [6]. The finite element approximation of the transformation is regularized by the linear elastic energy Fig.…”

Section: Registration Using Image Based Forcesmentioning

confidence: 99%

Improved Non-rigid Registration of Prostate MRI

d'Aische

Craene

Haker

et al. 2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. This paper introduces a fast non-rigid registration method for aligning pre-operative 1.5 T MR images to intra-operative 0.5 T MR images for guidance in prostate biopsy. After the acquisition of both pre-operative 1.5 T and intra-operative 0.5 T, an intensity correction method is applied to the pre-operative images to reduce the significant artifacts in the signal intensities due to the presence of an endo-rectal coil. A fast manual segmentation of prostate in both modalities is carried out to enable conformal mapping of the surface of the pre-operative data to the intra-operative data. A displacement field is estimated with a linear elastic inhomogeneous material model using the surface displacements established by the conformal mapping. We then use this as an initialization for a mutual information based non-rigid registration algorithm to match the internal structures of the prostate. This non-rigid registration is based on a finite element method discretization using the mutual information metric with a linear elastic regularization constraint. The registration is accelerated while preserving accuracy by using an adaptive mesh based on the body-centered cubic lattice and a significantly improved registration is achieved.

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Dense deformation field estimation for brain intraoperative images registration

Cited by 2 publications

References 9 publications

Efficient multi-modal dense field non-rigid registration: alignment of histological and section images

Efficient multi-modal dense field non-rigid registration: alignment of histological and section images

Improved Non-rigid Registration of Prostate MRI

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