Ultrasound Elastography Using Three Images

Displacement1 estimation is an essential step for ultrasound elastography and numerous techniques have been proposed to improve its quality using two frames of ultrasound RF data. This paper introduces a technique for calculating a displacement field from three frames of ultrasound RF data. To this end, we first introduce constraints on variations of the displacement field with time using mechanics of materials. These constraints are then used to generate a regularized cost function that incorporates amplitude similarity of three ultrasound images and displacement continuity. We optimize the cost function in an expectation maximization (EM) framework. Iteratively reweighted least squares (IRLS) is used to minimize the effect of outliers. We show that, compared to using two images, the new algorithm reduces the noise of the displacement estimation. The displacement field is used to generate strain images for quasi-static elastography. Phantom experiments and in-vivo patient trials of imaging liver tumors and monitoring thermal ablation therapy of liver cancer are presented for validation.

[1]  Andrew H. Gee,et al.  3D Elastography Using Freehand Ultrasound , 2004, MICCAI.

[2]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[3]  Septimiu E. Salcudean,et al.  Motion Estimation in Ultrasound Images Using Time Domain Cross Correlation With Prior Estimates , 2006, IEEE Transactions on Biomedical Engineering.

[4]  D De Ruysscher,et al.  Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. , 2010, Medical physics.

[5]  Gregory D. Hager,et al.  Ablation Monitoring with Elastography: 2D In-vivoand 3D Ex-vivoStudies , 2008, MICCAI.

[6]  Purang Abolmaesumi,et al.  Tissue typing using ultrasound RF time series: experiments with animal tissue samples. , 2010, Medical physics.

[7]  Gregory D. Hager,et al.  Real-Time Regularized Ultrasound Elastography , 2011, IEEE Transactions on Medical Imaging.

[8]  C. S. Spalding,et al.  In vivo real-time freehand palpation imaging. , 2003, Ultrasound in medicine & biology.

[9]  Andrew H. Gee,et al.  A data weighting scheme for quasistatic ultrasound elasticity imaging , 2010 .

[10]  Gregory D. Hager,et al.  Ultrasound Elastography: A Dynamic Programming Approach , 2008, IEEE Transactions on Medical Imaging.

[11]  M Fink,et al.  Measurement of viscoelastic properties of homogeneous soft solid using transient elastography: An inverse problem approach , 2004 .

[12]  S. Salcudean,et al.  Viscoelastic parameter estimation based on spectral analysis , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  Jingfeng Jiang,et al.  A novel image formation method for ultrasonic strain imaging. , 2007, Ultrasound in medicine & biology.

[14]  Yaoyao Cui,et al.  Elasticity reconstruction from displacement and confidence measures of a multi-compressed ultrasound RF sequence , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[15]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Christopher J. Taylor,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009 , 2009, Lecture Notes in Computer Science.

[17]  Gregory D. Hager,et al.  Tracked Regularized Ultrasound Elastography for Targeting Breast Radiotherapy , 2009, MICCAI.

[18]  Richard W Prager,et al.  An intelligent interface for freehand strain imaging. , 2008, Ultrasound in medicine & biology.

[19]  J. Greenleaf,et al.  Selected methods for imaging elastic properties of biological tissues. , 2003, Annual review of biomedical engineering.