Validation of nonrigid image registration using finite-element methods: application to breast MR images

Presents a novel method for validation of nonrigid medical image registration. This method is based on the simulation of physically plausible, biomechanical tissue deformations using finite-element methods. Applying a range of displacements to finite-element models of different patient anatomies generates model solutions which simulate gold standard deformations. From these solutions, deformed images are generated with a range of deformations typical of those likely to occur in vivo. The registration accuracy with respect to the finite-element simulations is quantified by co-registering the deformed images with the original images and comparing the recovered voxel displacements with the biomechanically simulated ones. The functionality of the validation method is demonstrated for a previously described nonrigid image registration technique based on free-form deformations using B-splines and normalized mutual information as a voxel similarity measure, with an application to contrast-enhanced magnetic resonance mammography image pairs. The exemplar nonrigid registration technique is shown to be of subvoxel accuracy on average for this particular application. The validation method presented here is an important step toward more generic simulations of biomechanically plausible tissue deformations and quantification of tissue motion recovery using nonrigid image registration. It will provide a basis for improving and comparing different nonrigid registration techniques for a diversity of medical applications, such as intrasubject tissue deformation or motion correction in the brain, liver or heart.

[1]  M. Viergever,et al.  Medical image matching-a review with classification , 1993, IEEE Engineering in Medicine and Biology Magazine.

[2]  Edward L. Chaney,et al.  Medical Image Synthesis via Monte Carlo Simulation , 2002, MICCAI.

[3]  Martin J. Yaffe,et al.  Biomechanical 3-D finite element modeling of the human breast using MRI data , 2001, IEEE Transactions on Medical Imaging.

[4]  Alejandro F. Frangi,et al.  Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration , 2001, MICCAI.

[5]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[6]  Karl Rohr,et al.  Approximating Thin-Plate Splines for Elastic Registration: Integration of Landmark Errors and Orientation Attributes , 1999, IPMI.

[7]  Derek L. G. Hill,et al.  Detecting Failure, Assessing Success , 2001 .

[8]  Simon R. Arridge,et al.  A survey of hierarchical non-linear medical image registration , 1999, Pattern Recognit..

[9]  D L Hill,et al.  Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. , 1997, Medical physics.

[10]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[11]  K. Rohr,et al.  Biomechanical modeling of the human head for physically based, nonrigid image registration , 1999, IEEE Transactions on Medical Imaging.

[12]  Colin Studholme,et al.  Visual assessment of the accuracy of retrospective registration of MR and CT images of the brain , 1998, IEEE Transactions on Medical Imaging.

[13]  Daniel Rueckert,et al.  Volume and Shape Preservation of Enhancing Lesions when Applying Non-rigid Registration to a Time Series of Contrast Enhancing MR Breast Images , 2000, MICCAI.

[14]  Derek L. G. Hill,et al.  Comparison of biomechanical breast models: a case study , 2002, SPIE Medical Imaging.

[15]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[16]  William J. Schroeder,et al.  The Visualization Toolkit , 2005, The Visualization Handbook.

[17]  Colin Studholme,et al.  Steps Toward a Stereo-Camera-Guided Biomechanical Model for Brain Shift Compensation , 2001, IPMI.

[18]  Michael I. Miga New approach to elastrograph imaging: modality-independent elastography , 2002, SPIE Medical Imaging.

[19]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[20]  Daniel Rueckert,et al.  Comparison and evaluation of rigid and nonrigid registration of breast MR images , 1999, Medical Imaging.

[21]  Ron Kikinis,et al.  Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model , 2001, IEEE Trans. Medical Imaging.

[22]  Joon B. Park,et al.  Biomaterials Science and Engineering , 1984, IEEE Transactions on Biomedical Engineering.

[23]  Michael I. Miga A new approach to elastographic imaging : Modality independent elastography , 2002 .

[24]  R. Sinkus,et al.  High-resolution tensor MR elastography for breast tumour detection. , 2000, Physics in medicine and biology.

[25]  R. Schwarzenberger,et al.  The finite element method : a first approach , 1981 .

[26]  J. Bishop,et al.  Visualization and quantification of breast cancer biomechanical properties with magnetic resonance elastography. , 2000, Physics in medicine and biology.

[27]  D. Rueckert,et al.  Comparison and evaluation of rigid, affine, and nonrigid registration of breast MR images. , 1999, Journal of computer assisted tomography.

[28]  Derek L. G. Hill,et al.  Finite-element based validation of nonrigid registration using single- and multilevel free-form deformations: application to contrast-enhanced MR mammography , 2002, SPIE Medical Imaging.

[29]  Sung Yong Shin,et al.  Scattered Data Interpolation with Multilevel B-Splines , 1997, IEEE Trans. Vis. Comput. Graph..

[30]  C. K. Yuen,et al.  Digital Filters , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[31]  A. Hartov,et al.  Model-updated image guidance: initial clinical experiences with gravity-induced brain deformation , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[32]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

[33]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[34]  Dimitris N. Metaxas,et al.  Methods for modeling and predicting mechanical deformations of the breast under external perturbations , 2002, Medical Image Anal..

[35]  M. Doyley,et al.  Evaluation of an iterative reconstruction method for quantitative elastography , 2000 .

[36]  Haiying Liu,et al.  Constructing Patient Specific Models for Correcting Intraoperative Brain Deformation , 2001, MICCAI.

[37]  Haiying Liu,et al.  A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations , 2001, MICCAI.

[38]  John Fitzpatrick Detecting Failure, Assessing Success , 2001 .

[39]  Dimitris N. Metaxas,et al.  A finite element model of the breast for predicting mechanical deformations during biopsy procedures , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[40]  David J. Hawkes,et al.  Validation of Non-rigid Registration Using Finite Element Methods , 2001, IPMI.

[41]  David J. Hawkes,et al.  Validation of Volume-Preserving Non-rigid Registration: Application to Contrast-Enhanced MR-Mammography , 2002, MICCAI.