Development of Less Computational Costly Ultrasound Imaging Using the Finite Element Method
Development of Less Computational Costly Ultrasound Imaging Using the Finite Element Method |
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© 2024 by IJETT Journal | ||
Volume-72 Issue-5 |
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Year of Publication : 2024 | ||
Author : Ahamed- Al- Arifin, S.M. Baque Billah, Kazi Muhammad Asif Ashrafi |
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DOI : 10.14445/22315381/IJETT-V72I5P131 |
How to Cite?
Ahamed- Al- Arifin, S.M. Baque Billah, Kazi Muhammad Asif Ashrafi, "Development of Less Computational Costly Ultrasound Imaging Using the Finite Element Method," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 299-312, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P131
Abstract
Elastography or strain imaging using ultrasound has been found to be useful for determining malignant tissue via state-of-the-art medical imaging. The strain is subjected to tissue displacement measurements. Most of the algorithms that are used to find tissue displacement via elastography are one-dimensional and direct strain imaging techniques are also computationally costly. To overcome these problems, this paper introduces a 2-D cross-correlation algorithm to compute the time delay in two directions and the workflow of the strain imaging has been modified to reduce the computation cost of imaging. The MATLAB tic toc function is used to determine the simulation time of each step of the modified workflow. To accomplish this work, a synthetic two-dimensional tissue-mimicking phantom was made by using the finite element-based software ANSYS. To obtain the radio frequency (RF) signal, different simulations were performed in the ultrasound simulation software FIELD II. The cross-correlation coefficient obtained from the ultrasound simulation was mapped using the MATLAB surf tool. The attained map shows an auspicious result in differentiating between benign and malignant tissue. Additionally, the proposed algorithm has a lower computational cost in terms of simulation time, with a value of 222.072322 seconds, in contrast with the simulation time of conventional strain imaging, which has a value of more than 222.093015 seconds. Therefore, by applying the above imaging algorithm and procedure to a real-world 3-D scenario, we may develop a more sophisticated imaging technique that is less computationally costly.
Keywords
Ultrasound, Elastography, FEM, 2D cross-correlation, Surf tool.
References
[1] Jonathan Ophir et al., “Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues,” Ultrasonic Imaging, vol. 13, pp. 111–134, 1991.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Andreas Heimdal et al., “Real-Time Strain Rate Imaging of the Left Ventricle by Ultrasound,” Journal of the American Society of Echocardiography, vol. 11, no. 11, pp. 1013–1019, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Laurence N. Bohs, Barry H. Friemel, and Gregg E. Trahey, “Experimental Velocity Profiles and Volumetric Flow via Two-Dimensional Speckle Tracking,” Ultrasound in Medicine & Biology, vol. 21, no. 7, pp. 885–898, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[4] M. Lubinski, S. Emelianov, and M. O’Donnell, “Speckle Tracking Methods for Ultrasonic Elasticity Imaging Using Short-Time Correlation,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 46, no. 1, pp. 82–96, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[5] S. Srinivasan, and Jonathan Ophir, “A Zero-Crossing Strain Estimator in Elastography,” Ultrasound in Medicine & Biology, vol. 29, no. 2, pp. 227–238, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[6] C. Kasai et al., “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” IEEE Transactions on Sonics and Ultrasonics, vol. 32, no. 3, pp. 458–464, 1985.
[CrossRef] [Google Scholar] [Publisher Link]
[7] T. Loupas, R.B. Peterson, and R.W. Gill, “Experimental Evaluation of Velocity and Power Estimation for Ultrasound Blood Flow Imaging, by Means of a Two-Dimensional Autocorrelation Approach,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 42, no. 4, pp. 689–699, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[8] H. Torp, K. Kristoffersen, and B.A.J. Angelsen, “Autocorrelation Techniques in Color Flow Imaging: Signal Model and Statistical Properties of the Autocorrelation Estimates,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 41, no. 5, pp. 604–612, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[9] F. Viola, and W.F. Walker, “A Comparison of the Performance of Time-Delay Estimators in Medical Ultrasound,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 50, no. 4, pp. 392–401, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[10] G.F. Pinton, J.J. Dahl, and G.E. Trahey, “Rapid Tracking of Small Displacements with Ultrasound,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, no. 6, pp. 1103–1117, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[11] I.A. Hein, and W.D. O’Brien, “Current Time-Domain Methods for Assessing Tissue Motion by Analysis from Reflected Ultrasound Echoes—A Review,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 40, no. 2, pp. 84–102, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Gianmarco F. Pinton, and Gregg E. Trahey, “Continuous Delay Estimation with Polynomial Splines,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, no. 11, pp. 2026–2035, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[13] G. Jacovitti, and G. Scarano, “Discrete Time Techniques for Time Delay Estimation,” IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 525–533, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[14] S. Langeland et al., “A Simulation Study on the Performance of Different Estimators for Two-Dimensional Velocity Estimation,” 2002 IEEE Ultrasonics Symposium, Proceedings, Munich, Germany, vol. 2, pp. 1859–1862, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M.B. Hisham et al., “Template Matching Using Sum of Squared Difference and Normalized Cross Correlation,” 2015 IEEE Student Conference on Research and Development (SCOReD), Kuala Lumpur, Malaysia, pp. 100-104, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hiroaki Niitsuma, and Tsutomu Maruyama, “Sum of Absolute Difference Implementations for Image Processing on FPGAs,” 2010 International Conference on Field Programmable Logic and Applications, Milan, Italy, pp. 167-170, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Anton Fertner, and Anders Sjolund, “Comparison of Various Time Delay Estimation Methods by Computer Simulation,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 5, pp. 1329-1330, 1986.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Isaac N. Bankman, Handbook of Medical Image Processing and Analysis, 2nd ed., Academic Press, Elsevier Science, Pages 435-452, 2009.
[Google Scholar] [Publisher Link]
[19] T. Varghese et al., “Tradeoffs in Elastographic Imaging,” Ultrasonic Imaging, vol. 23, no. 4, pp. 216–248, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Hairong Shi, and Tomy Varghese, “Two-Dimensional Multi-Level Strain Estimation for Discontinuous Tissue,” Physics in Medicine & Biology, vol. 52, no. 2, pp. 389–401, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[21] C. Sumi, “Fine Elasticity Imaging Utilizing the Iterative RF-echo Phase Matching Method,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 46, no. 1, pp. 158–166, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Reza Zahiri-Azar, and Septimiu E. Salcudean, “Time-Delay Estimation in Ultrasound Echo Signals Using Individual Sample Tracking,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 55, no. 12, pp. 2640-2650, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Jonathan Ophir et al., “Elastography: Ultrasonic Imaging of Tissue Strain and Elastic Modulus in Vivo,” European Journal of Ultrasound, vol. 3, no. 1, pp. 49–70, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[24] E.I. Céspedes et al., “Elastography: Elasticity Imaging Using Ultrasound with Application to Muscle and Breast in Vivo,” Ultrasonic Imaging, vol. 15, no. 2, pp. 73–88, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jonathan Ophir et al., “Elastography: Imaging the Elastic Properties of Soft Tissues with Ultrasound,” Journal of Medical Ultrasonics, vol. 29, pp. 155–171, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[26] R. Zahiri-Azar, and S.E. Salcudean, “Motion Estimation in Ultrasound Images Using Time Domain Cross Correlation with Prior Estimates,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 10, pp. 1990–2000, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[27] S. Kaisar Alam, “Novel Spline-Based Approach for Robust Strain Estimation in Elastography,” Ultrasonic Imaging, vol. 32, no. 2, pp. 91–102, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[28] S. Kaisar Alam, Jonathan Ophir, and E.E. Konofagou, “An Adaptive Strainestimator for Elastography,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 45, no. 2, pp. 461–472, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[29] S. Kaisar Alam et al., “Elastography: Ultrasonic Estimation and Imaging of the Elastic Properties of Tissues, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 213, no. 3, pp. 203–233, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Mohammad Arafat Hussain et al., “Direct and Gradient-Based Average Strain Estimation by Using Weighted Nearest Neighbor Cross-Correlation Peaks,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 59, no. 8, pp. 1713-1728, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[31] T. Varghese et al., “Direct Strain Estimation in Elastography Using Spectral Cross-Correlation,” Ultrasound in Medicine & Biology, vol. 26, no. 9, pp. 1525–1537, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[32] S. Kaisar Alam et al., “Adaptive Spectral Strain Estimators for Elastography,” Ultrasonic Imaging, vol. 26, no. 3, pp. 131–149, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Md. Maruf Hossain Shuvo, A.B.M. Aowlad Hossain, and Krishna Chandra Roy, “Ultrasound Strain Imaging Based on Information Theoretic Delay Estimation,” 2014 17th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, pp. 401-405, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Gérard Blanchet, and Maurice Charbit, Digital Signal and Image Processing Using MATLAB, Wiley-ISTE, 2006.
[Publisher Link]
[35] Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Digital Image Processing Using MATLAB, Gatesmark Publishing, 2020.
[Publisher Link]
[36] Jorgen Arendt Jensen, “Field: A Program for Simulating Ultrasound Systems,” 10th Nordic-Baltic Conference on Biomedical Imaging, Published in Medical & Biological Engineering & Computing, vol. 34, pp. 351-353, 1996.
[Google Scholar] [Publisher Link]
[37] Sen Zhong, Wei Xia, and Zishu He, “Approximate Maximum Likelihood Time Differences Estimation in the Presence of Frequency and Phase Consistence Errors,” IEEE International Symposium on Signal Processing and Information Technology, Athens, Greece, pp. 305-308, 2013.
[CrossRef] [Google Scholar] [Publisher Link]