Analysis of serial magnetic resonance images of mouse brains using image registration

The aim of this paper is to investigate techniques that can identify and quantify cross-sectional differences and longitudinal changes in vivo from magnetic resonance images of murine models of brain disease. Two different approaches have been compared. The first approach is a segmentation-based approach: Each subject at each time point is automatically segmented into a number of anatomical structures using atlas-based segmentation. This allows cross-sectional and longitudinal analyses of group differences on a structure-by-structure basis. The second approach is a deformation-based approach: Longitudinal changes are quantified by the registration of each subject's follow-up images to that subject's baseline image. In addition the baseline images can be registered to an atlas allowing voxel-wise analysis of cross-sectional differences between groups. Both approaches have been tested on two groups of mice: A transgenic model of Alzheimer's disease and a wild-type background strain, using serial imaging performed over the age range from 6-14 months. We show that both approaches are able to identify longitudinal and cross-sectional differences. However, atlas-based segmentation suffers from the inability to detect differences across populations and across time in regions which are much smaller than the anatomical regions. In contrast to this, the deformation-based approach can detect statistically significant differences in highly localized areas.

[1]  Christos Davatzikos,et al.  Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models , 1997, Comput. Vis. Image Underst..

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

[3]  Colin N. Dewey,et al.  Initial sequencing and comparative analysis of the mouse genome. , 2002 .

[4]  J. Richardson,et al.  Cognitive correlates of Aβ deposition in male and female mice bearing amyloid precursor protein and presenilin-1 mutant transgenes , 2004, Brain Research.

[5]  Jonathon Bishop,et al.  Magnetic resonance imaging for detection and analysis of mouse phenotypes , 2005, NMR in biomedicine.

[6]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[7]  Mouse Genome Sequencing Consortium.  Initial sequencing and comparative analysis of the mouse genome , 2002, Nature.

[8]  International Human Genome Sequencing Consortium.  Initial sequencing and analysis of the human genome , 2001, Nature.

[9]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[10]  Marc Dhenain,et al.  In vivo MRI and histological evaluation of brain atrophy in APP/PS1 transgenic mice , 2006, Neurobiology of Aging.

[11]  D. Louis Collins,et al.  Animal: Validation and Applications of Nonlinear Registration-Based Segmentation , 1997, Int. J. Pattern Recognit. Artif. Intell..

[12]  J. Richardson,et al.  Abeta deposition and related pathology in an APP x PS1 transgenic mouse model of Alzheimer's disease. , 2008, Histology and histopathology.

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

[14]  Natasa Kovacevic,et al.  Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRI , 2006, NeuroImage.

[15]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[16]  Alain Pitiot,et al.  A multimodal, multidimensional atlas of the C57BL/6J mouse brain , 2004, Journal of anatomy.

[17]  Nicholas T. Longford,et al.  Random Coefficient Models , 1994, International Encyclopedia of Statistical Science.

[18]  J. Roder,et al.  In Vivo Magnetic Resonance Imaging and Semiautomated Image Analysis Extend the Brain Phenotype for cdf/cdf Mice , 2006, The Journal of Neuroscience.

[19]  Alan C. Evans,et al.  A three-dimensional MRI atlas of the mouse brain with estimates of the average and variability. , 2005, Cerebral cortex.

[20]  Daniel Rueckert,et al.  An Evaluation of Deformation-Based Morphometry Applied to the Developing Human Brain and Detection of Volumetric Changes Associated with Preterm Birth , 2003, MICCAI.

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

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

[23]  Daniel Rueckert,et al.  Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration , 2004, IEEE Transactions on Medical Imaging.

[24]  Christos Davatzikos,et al.  Spatiotemporal maturation patterns of murine brain quantified by diffusion tensor MRI and deformation-based morphometry , 2005, Proc. Natl. Acad. Sci. USA.

[25]  Alejandro F Frangi,et al.  Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration , 2003, IEEE Transactions on Medical Imaging.

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

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

[28]  Anders M. Dale,et al.  Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain , 2005, NeuroImage.

[29]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[30]  Daniel Rueckert,et al.  Deformation Based Morphometry Analysis of Serial Magnetic Resonance Images of Mouse Brains , 2006, WBIR.

[31]  Karl J. Friston,et al.  Voxel-Based Morphometry , 2015 .

[32]  Daniel Rueckert,et al.  Automatic tracking of the aorta in cardiovascular MR images using deformable models , 1997, IEEE Transactions on Medical Imaging.

[33]  M. Dzugutov,et al.  addendum: A universal scaling law for atomic diffusion in condensed matter , 2001, Nature.