Morphological studies of the murine heart based on probabilistic and statistical atlases

This study directly compares morphological features of the mouse heart in its end-relaxed state based on constructed morphometric maps and atlases using principal component analysis in C57BL/6J (n=8) and DBA (n=5) mice. In probabilistic atlases, a gradient probability exists for both strains in longitudinal locations from base to apex. Based on the statistical atlases, differences in size (49.8%), apical direction (15.6%), basal ventricular blood pool size (13.2%), and papillary muscle shape and position (17.2%) account for the most significant modes of shape variability for the left ventricle of the C57BL/6J mice. For DBA mice, differences in left ventricular size and direction (67.4%), basal size (15.7%), and position of papillary muscles (16.8%) account for significant variability.

[1]  Alejandro F Frangi,et al.  Computational cardiac atlases: from patient to population and back , 2009, Experimental physiology.

[2]  Martin Styner,et al.  Automatic and Robust Computation of 3D Medial Models Incorporating Object Variability , 2003, International Journal of Computer Vision.

[3]  Mirza Faisal Beg,et al.  Computational cardiac anatomy using MRI , 2004, Magnetic resonance in medicine.

[4]  Anders M. Dale,et al.  Automated segmentation of the actively stained mouse brain using multi-spectral MR microscopy , 2008, NeuroImage.

[5]  Frederick H Epstein,et al.  MR in mouse models of cardiac disease , 2007, NMR in biomedicine.

[6]  Alejandro F. Frangi,et al.  Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling , 2002, IEEE Transactions on Medical Imaging.

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

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

[9]  Stefan Klein,et al.  Conditional Shape Models for Cardiac Motion Estimation , 2010, MICCAI.

[10]  H. Riedwyl,et al.  Multivariate Statistics: A Practical Approach , 1988 .

[11]  Sotirios A. Tsaftaris,et al.  2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) , 2013 .

[12]  Calvin R. Maurer,et al.  Statistical shape model generation using nonrigid deformation of a template mesh , 2005, SPIE Medical Imaging.

[13]  Paul M. Thompson,et al.  Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations , 1997, Medical Image Anal..

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

[15]  Rong Pan,et al.  Automated segmentation of mouse brain images using extended MRF , 2009, NeuroImage.

[16]  Paul M. Thompson,et al.  The role of image registration in brain mapping , 2001, Image Vis. Comput..

[17]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[18]  R Mark Henkelman,et al.  Systems biology through mouse imaging centers: experience and new directions. , 2010, Annual review of biomedical engineering.

[19]  A. Haase,et al.  Cardiovascular phenotype characterization in mice by high resolution magnetic resonance imaging , 2000, Magma: Magnetic Resonance Materials in Physics, Biology, and Medicine.

[20]  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.

[21]  Daniel Rueckert,et al.  A Dynamic Brain Atlas , 2002, MICCAI.

[22]  G Allan Johnson,et al.  4-D Micro-CT of the Mouse Heart , 2005, Molecular imaging.

[23]  Christopher J. Taylor,et al.  Building 3D sulcal models using local geometry , 2001, Medical Image Anal..

[24]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[25]  G Allan Johnson,et al.  High-resolution imaging of murine myocardial infarction with delayed-enhancement cine micro-CT. , 2007, American journal of physiology. Heart and circulatory physiology.

[26]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[27]  Yi Qi,et al.  Four‐dimensional MR microscopy of the mouse heart using radial acquisition and liposomal gadolinium contrast agent , 2008, Magnetic resonance in medicine.

[28]  K. Chien.  To Cre or not to Cre: the next generation of mouse models of human cardiac diseases. , 2001, Circulation research.

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

[30]  Milan Sonka,et al.  3-D active appearance models: segmentation of cardiac MR and ultrasound images , 2002, IEEE Transactions on Medical Imaging.

[31]  Dinggang Shen,et al.  Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms , 2006, NeuroImage.

[32]  R. M. Henkelman,et al.  Mouse embryonic phenotyping by morphometric analysis of MR images , 2010, Physiological genomics.

[33]  Yi Qi,et al.  4D micro-CT for cardiac and perfusion applications with view under sampling , 2011, Physics in medicine and biology.

[34]  Daniel Rueckert,et al.  Fast Spatio-temporal Free-Form Registration of Cardiac MR Image Sequences , 2004, FIMH.

[35]  Juha Koikkalainen,et al.  Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images , 2004, Medical Image Anal..

[36]  Alejandro F. Frangi,et al.  Bilinear Models for Spatio-Temporal Point Distribution Analysis: Application to Extrapolation of Whole Heart Cardiac Dynamics , 2007, ICCV.

[37]  Daniel Rueckert,et al.  Construction of a 4D Statistical Atlas of the Cardiac Anatomy and Its Use in Classification , 2005, MICCAI.

[38]  P A Narayana,et al.  High-resolution vascular imaging of the rat spine using liposomal blood pool MR agent. , 2007, AJNR. American journal of neuroradiology.

[39]  Hervé Delingette,et al.  A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts , 2007, IEEE Transactions on Medical Imaging.

[40]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[41]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[42]  Stefan Neubauer,et al.  Magnetic resonance microimaging for noninvasive quantification of myocardial function and mass in the mouse , 1998, Magnetic resonance in medicine.

[43]  L. Younes,et al.  Evidence of Structural Remodeling in the Dyssynchronous Failing Heart , 2005, Circulation research.

[44]  Alejandro F Frangi,et al.  Computational Anatomy Atlas of the Heart , 2007, 2007 5th International Symposium on Image and Signal Processing and Analysis.