Spherical volume-preserving Demons registration

In order to analyze the brain shift situation accurately, we need to register the medical image and analyze its deformation. In this paper, we introduce a framework with volume-preserving registration for brain shift analysis. First, a volume-preserving mapping is introduced for general manifolds supported by a rigorous continuous theory. The registration is then performed on the spherical tetrahedron mesh with MRI gray values. The registration can retain the equality of local volume elements while registering the manifold to a template at the same time. We use simulated brain shift data to test our method. The results show that our method can efficiently register the brain while preserving the volume of each vertex. We introduce a volume-preserving registration framework for brain shift analysis.A volume-preserving mapping is supported by a rigorous continuous theory.The registration is performed on spherical tetrahedron mesh with MRI gray value.The registration can retain the equality of local volume elements.Our method can register the brain efficiently while preserving the volume.

[1]  Dennis Spencer,et al.  Brain Shift Modeling for Use in Neurosurgery , 1998, MICCAI.

[2]  Jing Hua,et al.  Authalic Parameterization of General Surfaces Using Lie Advection , 2011, IEEE Transactions on Visualization and Computer Graphics.

[3]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[4]  Pedro V. Sander,et al.  Texture mapping progressive meshes , 2001, SIGGRAPH.

[5]  X. Zhang,et al.  Ricci flow-based spherical parameterization and surface registration , 2013, Comput. Vis. Image Underst..

[6]  Nobuhiko Hata,et al.  A Volumetric Optical Flow Method for Measurement of Brain Deformation from Intraoperative Magnetic Resonance Images , 1999, MICCAI.

[7]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

[8]  Peter Hastreiter,et al.  Strategies for brain shift evaluation , 2004, Medical Image Anal..

[9]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[10]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[11]  Colin P. McNally,et al.  Divergence-free interpolation of vector fields from point values — exact ∇ ⋅B = 0 in numerical simulations , 2011, 1102.4852.

[12]  N. Makris,et al.  MRI-Based Topographic Parcellation of Human Neocortex: An Anatomically Specified Method with Estimate of Reliability , 1996, Journal of Cognitive Neuroscience.

[13]  Shenghui Liao,et al.  Facial hexahedral mesh transferring by volumetric mapping based on harmonic fields , 2011, Comput. Graph..

[14]  P. Jaccard THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[15]  Mark Meyer,et al.  Intrinsic Parameterizations of Surface Meshes , 2002, Comput. Graph. Forum.

[16]  Dani Lischinski,et al.  Bounded-distortion piecewise mesh parameterization , 2002, IEEE Visualization, 2002. VIS 2002..

[17]  Pere Brunet Crosa,et al.  Volume-preserving deformation using generalized barycentric coordinates , 2010 .

[18]  Hong Qin,et al.  Meshless Harmonic Volumetric Mapping Using Fundamental Solution Methods , 2009, IEEE Transactions on Automation Science and Engineering.

[19]  Xin Zhao,et al.  Area-Preservation Mapping using Optimal Mass Transport , 2013, IEEE Transactions on Visualization and Computer Graphics.

[20]  Hong Qin,et al.  Geodesic Distance-weighted Shape Vector Image Diffusion , 2008, IEEE Transactions on Visualization and Computer Graphics.

[21]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.