Towards a Statistical Atlas of Cardiac Fiber Structure

We propose here a framework to build a statistical atlas of diffusion tensors of canine hearts. The anatomical images of seven hearts are first non-rigidly registered in the same reference frame and their associated diffusion tensors are then transformed with a method that preserves the cardiac laminar sheets. In this referential frame, the mean tensor and its covariance matrix are computed based on the Log-Euclidean framework. With this method, we can produce a smooth mean tensor field that is suited for fiber tracking algorithms or the electromechanical modeling of the heart. In addition, by examining the covariance matrix at each voxel it is possible to assess the variability of the cardiac fiber directions and of the orientations of laminar sheets. The results show a strong coherence of the diffusion tensors and the fiber orientations among a population of seven normal canine hearts.

[1]  Andrew S. Glassner,et al.  Graphics Gems , 1990 .

[2]  Ken Turkowski.  Properties of surface-normal transformations , 1990 .

[3]  P. Hunter,et al.  Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. , 1995, The American journal of physiology.

[4]  James C. Gee,et al.  Spatial transformations of diffusion tensor magnetic resonance images , 2001, IEEE Transactions on Medical Imaging.

[5]  A. McCulloch,et al.  Relating myocardial laminar architecture to shear strain and muscle fiber orientation. , 2001, American journal of physiology. Heart and circulatory physiology.

[6]  Derek K. Jones,et al.  Spatial Normalization and Averaging of Diffusion Tensor MRI Data Sets , 2002, NeuroImage.

[7]  Frank B. Sachse,et al.  Computational Cardiology , 2004, Lecture Notes in Computer Science.

[8]  Nicholas Ayache.  Computational Models for the Human Body , 2004 .

[9]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[10]  Michael I. Miller,et al.  Large deformation diffeomorphic metric mapping of fiber orientations , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Nicholas Ayache,et al.  Fast and Simple Calculus on Tensors in the Log-Euclidean Framework , 2005, MICCAI.

[12]  Guido Gerig,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II , 2005, MICCAI.

[13]  Hervé Delingette,et al.  Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging , 2005, Medical Image Anal..

[14]  N. Ayache,et al.  AN INTERACTIVE INTENSITY-AND FEATURE-BASED NON-RIGID REGISTRATION FRAMEWORK FOR 3 D MEDICAL IMAGES , 2005 .

[15]  L. Younes,et al.  Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure , 2005, Magnetic resonance in medicine.

[16]  Dinggang Shen,et al.  Estimating myocardial fiber orientations by template warping , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[17]  Hervé Delingette,et al.  Towards a Statistical Atlas of Cardiac Fiber Architecture , 2006 .

[18]  Nicholas Ayache,et al.  An interactive hybrid non-rigid registration framework for 3D medical images , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[19]  O. Faugeras,et al.  Non Rigid Registration of Diffusion Tensor Images , 2007 .