Regional Spatial Normalization: Toward an Optimal Target

Purpose The purpose of this work was to develop methods for defining, constructing, and evaluating a “minimal deformation target” (MDT) brain for multisubject studies based on analysis of the entire group. The goal is to provide a procedure that will create a standard, reproducible target brain image based on common features of a group of three-dimensional MR brain images. Method The average deformation and dispersion distance, derived from discrete three-dimensional deformation fields (DFs), are used to identify the best individual target (BIT) brain. This brain is assumed to be the one with the minimal deformation bias within a group of MR brain images. The BIT brain is determined as the one with the minimal target quality score, our cost function based on the deformation displacement and dispersion distance. The BIT brain is then transformed to the MDT brain using an average DF to create an optimized target brain. This analysis requires the calculation of a large number of DFs. To overcome this limitation, we developed an analysis method (the fast method) that reduces the task from order N2 complexity to one of order N, a tremendous advantage for large-N studies. Results Multiscale correlation analysis in a group of 20 subjects demonstrated the superiority of warping using the MDT target brain, made from the BIT brain, over several individual and MDT-transformed target brains also from the group. Conclusion Analysis of three-dimensional DF provides a means to quickly create a reproducible MDT target brain for any set of subjects. Warping to the MDT target was shown by an independent multiscale correlation method to produce superior results.

[1]  M. Raichle,et al.  A Stereotactic Method of Anatomical Localization for Positron Emission Tomography , 1985, Journal of computer assisted tomography.

[2]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[3]  Steven K. Feiner,et al.  Computer graphics: principles and practice (2nd ed.) , 1990 .

[4]  M. Mintun,et al.  Automated detection of the intercommissural line for stereotactic localization of functional brain images. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[5]  M I Miller,et al.  Mathematical textbook of deformable neuroanatomies. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[6]  K. Zilles,et al.  Human brain atlas: For high‐resolution functional and anatomical mapping , 1994, Human brain mapping.

[7]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[8]  D. Louis Collins,et al.  Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .

[9]  Jack L. Lancaster,et al.  A modality‐independent approach to spatial normalization of tomographic images of the human brain , 1995 .

[10]  P. Fox,et al.  Spatial normalization origins: Objectives, applications, and alternatives , 1995 .

[11]  A W Toga,et al.  Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers , 1996, Journal of magnetic resonance imaging : JMRI.

[12]  A. Toga,et al.  Three-Dimensional Statistical Analysis of Sulcal Variability in the Human Brain , 1996, The Journal of Neuroscience.

[13]  Thomas Ertl,et al.  Computer Graphics - Principles and Practice, 3rd Edition , 2014 .

[14]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[15]  J L Lancaster,et al.  Quantification of white matter and gray matter volumes from three‐dimensional magnetic resonance volume studies using fuzzy classifiers , 1998, Journal of magnetic resonance imaging : JMRI.

[16]  P. Fox,et al.  Global spatial normalization of human brain using convex hulls. , 1999, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[17]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[18]  Jack L. Lancaster,et al.  Accurate High-Speed Spatial Normalization Using an Octree Method , 1999, NeuroImage.

[19]  Alan C. Evans,et al.  Animal: Automatic Nonlinear Image Matching and Anatomical Labeling , 1999 .

[20]  R. Woods,et al.  Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain , 2000, Human brain mapping.

[21]  P Kochunov,et al.  Evaluation of octree regional spatial normalization method for regional anatomical matching , 2000, Human brain mapping.