Bimodal Nonrigid Registration of Brain MRI Data With Deconvolution of Joint Statistics

A brain MRI protocol typically includes several imaging contrasts that can provide complementary information by highlighting different tissue properties. The acquired data sets often need to be coregistered or placed in a standard anatomic space before any further processing. Current registration methods particularly for multicontrast data are computationally very intensive, their resolution is lower than that of the images, and their distance metric and its optimization can be limiting. In this paper, a novel and effective nonrigid registration method is proposed that is based on the restoration of the joint statistics of pairs of such images. The registration is performed with the deconvolution of the joint statistics with an adaptive Wiener filter. The deconvolved statistics are forced back to the spatial domain to estimate a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared with the B-Splines method implemented in 3DSlicer and with the SyN method implemented in the ANTs toolkit. The validation has been performed with a simulated Shepp-Logan phantom, a BrainWeb phantom, the real data of the NIREP database, and real multicontrast data sets of healthy volunteers. The proposed method has shown improved comparative accuracy as well as analytical efficiency.

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

[2]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[3]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[4]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[5]  Colin Studholme,et al.  Using Voxel Similarity as a Measure of Medical Image Registration , 1994, BMVC.

[6]  Ron Kikinis,et al.  3D Slicer , 2012, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

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

[8]  Albert C. S. Chung,et al.  Non-rigid image registration of brain magnetic resonance images using graph-cuts , 2011, Pattern Recognit..

[9]  Albert C. S. Chung,et al.  Non-rigid image registration by using graph-cuts with mutual information , 2010, 2010 IEEE International Conference on Image Processing.

[10]  Daniel Rueckert,et al.  Diffeomorphic Registration Using B-Splines , 2006, MICCAI.

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

[12]  Paul W. Fieguth,et al.  Adaptive Wiener filtering of noisy images and image sequences , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[14]  Nathan D. Cahill,et al.  Fourier Methods for Nonparametric Image Registration , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Philippe C. Cattin,et al.  Multi-modal diffeomorphic demons registration based on point-wise mutual information , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Sébastien Ourselin,et al.  Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images , 2010, Medical Imaging.

[17]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..

[19]  Olivier D. Faugeras,et al.  Variational Methods for Multimodal Image Matching , 2002, International Journal of Computer Vision.

[20]  Aria Nosratinia,et al.  Image denoising via wavelet-domain spatially adaptive FIR Wiener filtering , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[21]  Albert C. S. Chung,et al.  Non-rigid Image Registration Using Graph-cuts , 2007, MICCAI.

[22]  Nikos Paragios,et al.  DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting , 2009, IPMI.

[23]  Colin Studholme,et al.  Human brain labeling using image similarities , 2011, CVPR 2011.

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

[25]  Luis Ibáñez,et al.  The ITK Software Guide , 2005 .

[26]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[27]  Imran A. Pirwani,et al.  Introduction to the Non-rigid Image Registration Evaluation Project (NIREP) , 2006, WBIR.

[28]  F. H. Adler.  Cybernetics, or Control and Communication in the Animal and the Machine. , 1949 .

[29]  Anne L. Martel,et al.  Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast , 2007, Physics in medicine and biology.

[30]  David R. Haynor,et al.  Nonrigid multimodality image registration , 2001, SPIE Medical Imaging.

[31]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[32]  Colin Studholme,et al.  Voxel similarity measures for automated image registration , 1994, Other Conferences.