Cardiac motion estimation in ultrasound images using spatial and sparse regularizations

This paper investigates a new method for cardiac motion estimation in 2D ultrasound images. The motion estimation problem is formulated as an energy minimization with spatial and sparse regularizations. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated in terms of motion estimation and strain accuracy and compared with state-of-the-art algorithms using a dataset of realistic simulations. These simulation results show that the proposed method provides very promising results for myocardial motion estimation.

[1]  Olivier Bernard,et al.  Myocardial Motion Estimation From Medical Images Using the Monogenic Signal , 2013, IEEE Transactions on Image Processing.

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

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

[4]  Xiaogang Wang,et al.  Optical flow estimation using learned sparse model , 2011, 2011 International Conference on Computer Vision.

[5]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[6]  Jean-Yves Tourneret,et al.  Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning , 2018, IEEE Transactions on Image Processing.

[7]  W. Walker,et al.  A comparison of the performance of time-delay estimators in medical ultrasound , 2003, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  Its'hak Dinstein,et al.  New maximum likelihood motion estimation schemes for noisy ultrasound images , 2002, Pattern Recognit..

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

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

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

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

[13]  T. Abraham,et al.  Role of tissue Doppler and strain echocardiography in current clinical practice. , 2007, Circulation.

[14]  C. Lamberti,et al.  Evaluation of differential optical flow techniques on synthesized echo images , 1996, IEEE Transactions on Biomedical Engineering.

[15]  Ying Wu,et al.  Sparsity model for robust optical flow estimation at motion discontinuities , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  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.

[17]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[18]  Michael Elad,et al.  Expected Patch Log Likelihood with a Sparse Prior , 2014, EMMCVPR.

[19]  Lei Zhang,et al.  Sparsity-based image denoising via dictionary learning and structural clustering , 2011, CVPR 2011.

[20]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[21]  Hervé Delingette,et al.  Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings , 2016, IEEE Transactions on Medical Imaging.

[22]  Guillermo Sapiro,et al.  Online dictionary learning for sparse coding , 2009, ICML '09.