Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography

Quantitative analysis of left ventricular deformation can provide valuable information about the extent of disease as well as the efficacy of treatment. In this work, we develop an adaptive multi-level compactly supported radial basis approach for deformation analysis in 3D+time echocardiography. Our method combines displacement information from shape tracking of myocardial boundaries (derived from B-mode data) with mid-wall displacements from radio-frequency-based ultrasound speckle tracking. We evaluate our methods on open-chest canines (N=8) and show that our combined approach is better correlated to magnetic resonance tagging-derived strains than either individual method. We also are able to identify regions of myocardial infarction (confirmed by postmortem analysis) using radial strain values obtained with our approach.

[1]  W. Stevenson,et al.  Characterization of the Peri-Infarct Zone by Contrast-Enhanced Cardiac Magnetic Resonance Imaging Is a Powerful Predictor of Post–Myocardial Infarction Mortality , 2006, Circulation.

[2]  R. Leahy,et al.  Computation of 3-D velocity fields from 3-D cine CT images of a human heart. , 1991, IEEE transactions on medical imaging.

[3]  Victor Mor-Avi,et al.  Current and evolving echocardiographic techniques for the quantitative evaluation of cardiac mechanics: ASE/EAE consensus statement on methodology and indications endorsed by the Japanese Society of Echocardiography. , 2011, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[4]  Alejandro F. Frangi,et al.  Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography , 2012, Medical Image Anal..

[5]  Hon Fai Choi,et al.  Regional cardiac motion and strain estimation in three-dimensional echocardiography: a validation study in thick-walled univentricular phantoms , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[6]  James S. Duncan,et al.  A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography , 2012, MICCAI.

[7]  T. Varghese,et al.  Comparison of cardiac displacement and strain imaging using ultrasound radiofrequency and envelope signals. , 2013, Ultrasonics.

[8]  M. O’Donnell,et al.  Internal displacement and strain imaging using ultrasonic speckle tracking , 1994, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[9]  M. Aliabadi,et al.  The Boundary Element Method , 2002 .

[10]  James S. Duncan,et al.  Comparing Shape Tracking, Speckle Tracking, and a Combined Method for Deformation Analysis in Echocardiography , 2011, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology.

[11]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[12]  James S. Duncan,et al.  Combining shape and speckle tracking for deformation analysis in echocardiography using radial basis functions , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  James S. Duncan,et al.  Volumetric Deformation Analysis Using Mechanics-Based Data Fusion: Applications in Cardiac Motion Recovery , 1999, International Journal of Computer Vision.

[14]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[15]  Oskar M. Skrinjar,et al.  Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model , 2008, Medical Image Anal..

[16]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[17]  Alan S. Willsky,et al.  Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images , 2005, IPMI.

[18]  Dorin Comaniciu,et al.  3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  E. McVeigh,et al.  Three-dimensional systolic strain patterns in the normal human left ventricle: characterization with tagged MR imaging. , 2000, Radiology.

[20]  James S. Duncan,et al.  Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor , 2012, Medical Image Anal..

[21]  C. Kramer,et al.  Remote noninfarcted region dysfunction soon after first anterior myocardial infarction. A magnetic resonance tagging study. , 1996, Circulation.

[22]  Peter Boesiger,et al.  Myocardial tagging with 3D‐CSPAMM , 2002, Journal of magnetic resonance imaging : JMRI.

[23]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[24]  Jerry L Prince,et al.  Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[25]  Michael Unser,et al.  Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation , 2005, IEEE Transactions on Medical Imaging.

[26]  Richard G P Lopata,et al.  Three-dimensional cardiac strain imaging in healthy children using RF-data. , 2011, Ultrasound in medicine & biology.

[27]  Jerry L Prince,et al.  Visualizing myocardial function using HARP MRI. , 2000, Physics in medicine and biology.

[28]  James S. Duncan,et al.  Estimation of 3-D left ventricular deformation from medical images using biomechanical models , 2002, IEEE Transactions on Medical Imaging.

[29]  Sheng-Wen Huang,et al.  Analysis of correlation coefficient filtering in elasticity imaging , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[30]  James S. Duncan,et al.  Boundary element method-based regularization for recovering of LV deformation , 2007, Medical Image Anal..

[31]  Martin D. Buhmann,et al.  Radial Basis Functions: Theory and Implementations: Preface , 2003 .

[32]  James S. Duncan,et al.  Generalized robust point matching using an extended free-form deformation model: application to cardiac images , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[33]  A. Støylen,et al.  Noninvasive myocardial strain measurement by speckle tracking echocardiography: validation against sonomicrometry and tagged magnetic resonance imaging. , 2006, Journal of the American College of Cardiology.

[34]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Sabine Coquillart,et al.  Extended free-form deformation: a sculpturing tool for 3D geometric modeling , 1990, SIGGRAPH.

[36]  Wei-Ning Lee,et al.  In vivo study of myocardial elastography under graded ischemia conditions , 2011, Physics in medicine and biology.

[37]  Dimitris N. Metaxas,et al.  Preliminary validation of angle-independent myocardial elastography using MR tagging in a clinical setting. , 2008, Ultrasound in medicine & biology.

[38]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[39]  Milan Sonka,et al.  Automatic segmentation of echocardiographic sequences by active appearance motion models , 2002, IEEE Transactions on Medical Imaging.

[40]  M. O’Donnell,et al.  Strain rate imaging using two-dimensional speckle tracking , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[41]  Qi Duan,et al.  Comparing optical-flow based methods for quantification of myocardial deformations on RT3D ultrasound , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[42]  J. Lowe,et al.  The Wavefront Phenomenon of Ischemic Cell Death: 1. Myocardial Infarct Size vs Duration of Coronary Occlusion in Dogs , 1977, Circulation.

[43]  K. R. Raghavan,et al.  Lateral displacement estimation using tissue incompressibility , 1996, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[44]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[45]  J.T. Powers,et al.  An axial velocity estimator for ultrasound blood flow imaging, based on a full evaluation of the Doppler equation by means of a two-dimensional autocorrelation approach , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[46]  Paul Suetens,et al.  Three-Dimensional Cardiac Strain Estimation Using Spatio–Temporal Elastic Registration of Ultrasound Images: A Feasibility Study , 2008, IEEE Transactions on Medical Imaging.

[47]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  James S. Duncan,et al.  A robust point-matching algorithm for autoradiograph alignment , 1997, Medical Image Anal..

[49]  A. Young,et al.  Two‐dimensional Left Ventricular Deformation During Systole Using Magnetic Resonance Imaging With Spatial Modulation of Magnetization , 1994, Circulation.

[50]  James S. Duncan,et al.  Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences , 2000, IEEE Transactions on Medical Imaging.

[51]  James S. Duncan,et al.  A Boundary Element-Based Approach to Analysis of LV Deformation , 2005, MICCAI.

[52]  L. Axel,et al.  Myocardial function in infarcted and remote regions early after infarction in man: Assessment by magnetic resonance tagging and strain analysis , 1997, Magnetic resonance in medicine.

[53]  Kevin Kit Parker,et al.  Mechanotransduction: the role of mechanical stress, myocyte shape, and cytoskeletal architecture on cardiac function , 2011, Pflügers Archiv - European Journal of Physiology.

[54]  E. Zerhouni,et al.  Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion. , 1988, Radiology.

[55]  Ben A. Lin,et al.  3D Elasticity imaging using principal stretches on an open-chest dog heart , 2010, 2010 IEEE International Ultrasonics Symposium.

[56]  Reza Nezafat,et al.  Left ventricular infarct size, peri‐infarct zone, and papillary scar measurements: A comparison of high‐resolution 3D and conventional 2D late gadolinium enhancement cardiac MR , 2009, Journal of magnetic resonance imaging : JMRI.

[57]  Miguel Á. Carreira-Perpiñán,et al.  Non-rigid point set registration: Coherent Point Drift , 2006, NIPS.

[58]  M.A. Lubinski,et al.  Speckle tracking methods for ultrasonic elasticity imaging using short-time correlation , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[59]  L Axel,et al.  Regional differences in function within noninfarcted myocardium during left ventricular remodeling. , 1993, Circulation.

[60]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[61]  X. Zeng,et al.  Geometric strategies for neuroanatomic analysis from MRI , 2004, NeuroImage.

[62]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

[63]  Holger Wendland,et al.  Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree , 1995, Adv. Comput. Math..

[64]  F. Epstein.  MRI of left ventricular function , 2007, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[65]  James S. Duncan,et al.  Segmentation of left ventricles from echocardiographic sequences via sparse appearance representation , 2012, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.

[66]  Laurent D. Cohen,et al.  A Parametric Deformable Model to Fit Unstructured 3D Data , 1998, Comput. Vis. Image Underst..

[67]  L. Axel,et al.  MR imaging of motion with spatial modulation of magnetization. , 1989, Radiology.

[68]  James S. Duncan,et al.  Multi-frame Radial Basis Functions to Combine Shape and Speckle Tracking for Cardiac Deformation Analysis in Echocardiography , 2011, FIMH.

[69]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[70]  L.N. Bohs,et al.  A novel method for angle independent ultrasonic imaging of blood flow and tissue motion , 1991, IEEE Transactions on Biomedical Engineering.

[71]  Andriy Myronenko,et al.  Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates , 2009, FIMH.

[72]  James S. Duncan,et al.  A combined shape tracking and speckle tracking approach for 4D deformation analysis in echocardiography , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[73]  E. Konofagou,et al.  Physiologic cardiovascular strain and intrinsic wave imaging. , 2011, Annual review of biomedical engineering.

[74]  James S. Duncan,et al.  A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography , 2009, MICCAI.

[75]  J. Lima,et al.  Cardiovascular magnetic resonance characterization of peri-infarct zone remodeling following myocardial infarction , 2012, Journal of Cardiovascular Magnetic Resonance.

[76]  James S. Duncan,et al.  Estimation of 3D left ventricular deformation from echocardiography , 2001, Medical Image Anal..