Adaptive registration of magnetic resonance images based on a viscous fluid model

This paper develops a new viscous fluid registration algorithm that makes use of a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid governed by the nonlinear Navier-Stokes partial differential equation (PDE) that varies in both temporal and spatial domains. We replace the pressure term with an image-based body force to guide the transformation that is weighted by the mutual information between the template and reference images. A computationally efficient algorithm with staggered grids is introduced to obtain stable solutions of this modified PDE for transformation. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information reaches a prescribed threshold. We have evaluated this new algorithm in a number of synthetic and medical images. As consistent with the theory of the viscous fluid model, we found that our method faithfully transformed the template images into the reference images based on the intensity flow. Experimental results indicated that the proposed scheme achieved stable registrations and accurate transformations, which is of potential in large-scale medical image deformation applications.

[1]  Jinkoo Kim,et al.  A finite element method to correct deformable image registration errors in low-contrast regions , 2012, Physics in medicine and biology.

[2]  D. Hawkes,et al.  Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging , 2005, Physics in medicine and biology.

[3]  Zhou Jian,et al.  Fast Non-Rigid Image Registration Using Viscous Fluid Model and B-Spline , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[4]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[5]  Paul M. Thompson,et al.  Fluid Registration of Diffusion Tensor Images Using Information Theory , 2008, IEEE Transactions on Medical Imaging.

[6]  Yuanyuan Wang,et al.  Multiscaled combination of MR and SPECT images in neuroimaging: A simplex method based variable-weight fusion , 2012, Comput. Methods Programs Biomed..

[7]  C. Studholme,et al.  Intensity Robust Viscous Fluid Deformation Based Morphometry Using Regionally Adapted Mutual Information , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[8]  Mark Holden,et al.  A Review of Geometric Transformations for Nonrigid Body Registration , 2008, IEEE Transactions on Medical Imaging.

[9]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[10]  Nick C. Fox,et al.  Improved reliability of hippocampal atrophy rate measurement in mild cognitive impairment using fluid registration , 2007, NeuroImage.

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

[12]  J. Marsden,et al.  A mathematical introduction to fluid mechanics , 1979 .

[13]  Soichiro Tokuhisa,et al.  Automatic parameter regulation for CT/MRA viscous fluid registration , 2010, TENCON 2010 - 2010 IEEE Region 10 Conference.

[14]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

[15]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[16]  Erlend Hodneland,et al.  Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging , 2012, Comput. Methods Programs Biomed..

[17]  Xiaoyan Xu,et al.  Fast fluid registration using inverse filtering for non-rigid image registration , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[18]  Gerald E. Farin,et al.  Image registration using hierarchical B-splines , 2004, IEEE Transactions on Visualization and Computer Graphics.

[19]  Xuesong Lu,et al.  SIFT and shape information incorporated into fluid model for non-rigid registration of ultrasound images , 2010, Comput. Methods Programs Biomed..

[20]  Jan Modersitzki,et al.  Numerical Methods for Image Registration , 2004 .

[21]  Paul Suetens,et al.  A viscous fluid model for multimodal non-rigid image registration using mutual information , 2003, Medical Image Anal..

[22]  Cornelis H. Slump,et al.  MRI modalitiy transformation in demon registration , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[24]  R. Rabbitt,et al.  3D brain mapping using a deformable neuroanatomy. , 1994, Physics in medicine and biology.

[25]  Xuesong Lu,et al.  Mutual information-based multimodal image registration using a novel joint histogram estimation , 2008, Comput. Medical Imaging Graph..

[26]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[27]  Nick C Fox,et al.  Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. , 1998, Journal of computer assisted tomography.

[28]  Paul M. Thompson,et al.  A Tensor-Based Morphometry Study of Genetic Influences on Brain Structure Using a New Fluid Registration Method , 2008, MICCAI.

[29]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[30]  Morten Bro-Nielsen,et al.  Fast Fluid Registration of Medical Images , 1996, VBC.

[31]  Jingwen Yan,et al.  Image registration based on criteria of feature point pair mutual information , 2011 .