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| dc.contributor.author | Al-Arifin, Ahamed | |
| dc.date.accessioned | 2022-04-17T05:25:40Z | |
| dc.date.available | 2022-04-17T05:25:40Z | |
| dc.date.issued | 2021-10-30 | |
| dc.identifier.citation | [1] Timoshenko, S. P. and Goodier, J. N. Theory of Elasticity, pp. 403–409 (McGraw-Hill, New York), 1970. [2] Cheney, M. Inverse boundary-value problems. Am. Scientist, 85, pp. 448–455, 1997. [3] Carton, R. W., Clark, J. W., Barron, A. and Dainauskas, J. Estimation of tissue elasticity of the lung. J. Appl. Physiol.,19, pp. 236–242, 1964. [4] Hayes, W. C. and Mockros, L. F. Viscoelastic properties of human articular cartilage. J. Appl. Physiol., 1971, 31, 562–568. [5] Krokosky, E. M. and Krouskop, T. A. A stress deformation model of the human aorta. J. Biomed. Mater. Res., 2, pp. 503–525, 1968. [6] Krokosky, E. M. and Krouskop, T. A. The determination of the constitutive relationships for a human aorta. J. Biomed. Mater. Res., 4, pp. 525–547, 1970. [7] Md. Maruf Hossain Shuvo, A. B. M. Aowlad Hossain, and Krishna Chandra Roy, “Ultrasound Strain Imaging Based on Information Theoretic Delay Estimation,” 17th Int'l Conf. on Computer and Information Technology, Daffodil International University, Dhaka, Bangladesh, pp. 401-405, 22-23 December 2014. [8] J. Ophir, I. Cespedes, H. Ponnekanti, Y. Yazdi, and X. Li, “Elastography: A quantitative method for imaging the elasticity of biological tissues,” Ultrason. Imaging, vol. 13, pp. 111–134, Apr. 1991. [9] A. Heimdal, A. Stoylen, H. Torp, and T. Skjaerpe, “Real-time strain rate imaging of the left ventricle by ultrasound,” J. Am. Soc. Echocardiogr., vol. 11, pp. 1013–1019, Nov. 1998. [10] L. N. Bohs, B. H. Friemel, and G. E. Trahey, “Experimental velocity profiles and volumetric flow via two-dimensional speckle tracking,” Ultrasound Med. Biol., vol. 21, no. 7, pp. 885–898, 1995. [11] M. Lubinski, S. Emelianov, and M. O’Donnell, “Speckle tracking methods for ultrasonic elasticity imaging using short-time correlation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 46, pp. 82–96, Jan. 1999. [12] S. Srinivasan and J. Ophir, “A zero-crossing strain estimator in elastography,” Ultrasound Med. Biol., vol. 29, no. 2, pp. 227–238, Feb. 2003. [13] C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-time two-dimensional blood flow imaging using an autocorrelation technique,” IEEE Trans. Sonics Ultrason., vol. 32, no. 3, pp. 458–464, 1985. 82 [14] T. Loupas, R. Peterson, and R. Gill, “Experimental evaluation of velocity and power estimation for ultrasound blood flow imaging, by means of a two-dimensional autocorrelation approach,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 42, pp. 689–699, Jul. 1995. [15] H. Torp, K. Kristoffersen, and B. Angelsen, “Autocorrelation techniques in color flow imaging. Signal model and statistical properties of the autocorrelation estimates,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 41, no. 5, pp. 604–612, Sep. 1994. [16] F. Viola and W. Walker, “A comparison of the performance of time-delay estimators in medical ultrasound,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 50, pp. 392–401, Apr. 2003. [17] G. Pinton, J. Dahl, and G. Trahey, “Rapid tracking of small displacements with ultrasound,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 53, pp. 1103–1117, June 2006. [18] I. Hein and W. O’Brien, “Current time-domain methods for assessing tissue motion by analysis from reflected ultrasound echoes—A review,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 40, pp. 84–102, Mar. 1993. [19] G. Pinton and G. Trahey, “Continuous delay estimation with polynomial splines,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 53, pp. 2026–2035, Nov. 2006. [20] G. Jacovitti and G. Scarano, “Discrete time techniques for time delay estimation,” IEEE Trans. Acoust. Speech Signal Process., vol. 41, pp. 525–533, Feb. 1993. [21] S. Langeland, J. Dapos, H. Torp, B. Bijnens, and P. Suetens, “A simulation study on the performance of different estimators for twodimensional velocity estimation,” in Proc. IEEE Ultrasonics Symp., vol. 2, pp. 1859–1862, Oct. 8–11, 2002. [22] M. B. Hisham, Shahrul Nizam Yaakob, Raof R. A. A, A. B A. Nazren, N.M.Wafi, “Template Matching Using Sum of Squared Difference and Normalized Cross Correlation”, IEEE Student Conference on Research and Development (SCOReD),pp 100-104, 2015. [23] Hiroaki Niitsuma, Tsutomu Maruyama, “Sum of Absolute Difference Implementations for image processing on FPGAs”, 2010 International Conference on Field Programmable Logic and Applications, pp 167-170. [24] Anton1 Fertner and Anders Sjolund, “Comparison of Various Time Delay Estimation Methods by Computer Simulation”, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-34, no. 5, October 1986, pp 1329-1330. [25] https://en.wikipedia.org/wiki/Cross-correlation. [26] T. Varghese, J. Ophir, E. Konofagou, F. Kallel, and R. Righetti, “Tradeoffs in elastographic imaging, ultrasonic imaging,” Ultrason. Imaging, vol. 23, pp. 216–248, Oct. 2001. 83 [27] H. Shi and T. Varghese, “Two-dimensional multi-level strain estimation for discontinuous tissue,” Phys. Med. Biol., vol. 52, pp. 389–401, Nov. 2007. [28] C. Sumi, “Fine elasticity imaging utilizing the iterative RF-echo phase matching method,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 46, pp. 158–166, Jan. 1999. [29] Reza Zahiri-Azar, 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, December 2008. [30] Mohammad Arafat Hussain, Emran Mohammad Abu Anas, S. Kaisar Alam, Soo Yeol Lee, and Md. Kamrul Hasan, “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, August 2012. [31] J. Ophir, I. Cespedes, B. S. Garra, H. Ponnekanti, Y. Huanga, and N. Maklad, “Elastography: Ultrasonic imaging of tissue strain and elastic modulus in vivo,” Eur. J. Ultrasound, vol. 3, no. 1, pp. 49–70,1996. [32] E. I. Céspedes, J. Ophir, H. Ponnekanti, and N. Maklad, “Elastography: Elasticity imaging using ultrasound with application to muscle and breast in vivo,” Ultrason. Imaging, vol. 15, no. 2, pp. 73–88, 1993. [33] J. Ophir, S. K. Alam, B. S. Garra, F. Kallel, E. E. Konofagou, T. Krouskop, C. R. B. Merritt, R. Righetti, R. Souchon, S. Srinivasan, and T. Varghese, “Elastography: Imaging the elastic properties of soft tissues with ultrasound,” J. Med. Ultrasound, vol. 29, pp.155–171, Winter 2002. [34] R. Zahiri-Azar and S. E. Salcudean, “Motion estimation in ultrasound images using time domain cross correlation with prior estimates,” IEEE Trans. Biomed. Eng., vol. 53, no. 10, pp. 1990–2000, 2006. [35] S. K. Alam, “Novel spline-based approach for robust strain estimation in elastography,” Ultrason. Imaging, vol. 32, no. 2, pp. 91–102, 2010. [36] S. K. Alam, J. Ophir, and E. E. Konofagou, “An adaptive strainestimator for elastography,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 45, no. 2, pp. 461–472, 1998. [37] J. Ophir, S. K. Alam, B. S. Garra, F. Kallel, E. E. Konofagou, T. Krouskop, and T. Varghese, “Elastography: Ultrasonic estimation and imaging of the elastic properties of tissues,’’ in Proc. of the Institute of Mechanical Engineers, Part H: J. Eng. Medicine, vol. 213, no. 3, pp. 203–233, 1999. [38] T. Varghese, E. E. Konofagou, J. Ophir, S. K. Alam, and M. Bilgen, “Direct strain estimation in elastography using spectral cross-correlation,” Ultrasound Med. Biol., vol. 26, no. 9, pp. 1525–1537, 2000. 84 [39] S. K. Alam, F. L. Lizzi, T. Varghese, E. J. Feleppa, and S. Ramachandran, “Adaptive spectral strain estimators for elastography,” Ultrason. Imaging, vol. 26, no. 3, pp. 131–149, 2004. [40] Gérard Blanchet and Maurice Charbit, “Digital Signal and Image Processing Using MATLAB”, 2nd edition, Wiley-ISTE, May 22, 2006. [41] R. C. Gonzalez (Author), R. E. Woods, and S. L. Eddins, “Digital Image Processing Using MATLAB”, 3rd Edition, Gatesmark, January 1, 2020. [42] Ultrasonic Transducers Technical Notes, (2011), olympus-ims, pp. 41-48. https://www.olympus-ims.com/en/resources/white-papers/ultrasonic-transducer-technicalnotes/ [43] Carol Rumack and Deborah Levine, “Diagnostic Ultrasound”, 5th Edition, Elsevier, August 8, 2017. [44] D. Kane, W. Grassi, R. Sturrock, and P. V. Balint, “A brief history of musculoskeletal ultrasound: From bats and ships to babies and hips”, Rheumatology, Volume 43, Issue 7, July 1, 2004. [45] Micheal L Oelze, “Chapter 5-The Acoustic Wave Equation and Simple Solutions”, University of Illinois Urbana-Champaign. [46] E. Okuno, “Chapter-12 Diagnostic Radiology Physics: A Handbook for Teachers and Student”, Sao Paulo University. [47] C. Fung, Biomechanics: Mechanical Properties of Living Tissues, 2nd ed. Springer-Verlag, New York, 1993. [48] Goksel, R. Zahiri-Azar, and S.E. Salcudean, “Simulation of ultrasound radio-frequency signals in deformed tissue for validation of 2d motion estimation with sub-sample accuracy,” Proceedings of the IEEE Eng. Med.Biol. Soc., 2007. [49] Dimitris Manolakis and John G Proakis, “Digital Signal Processing: Principles, Algorithms, and Applications” 4th Edition,Prentice Hall, October 5, 1995,pp.116-120. [50] Buck, John R., Michael M. Daniel, and Andrew C. Singer. “Computer Explorations in Signals and Systems Using MATLAB”, 2nd Edition. Upper Saddle River, NJ: Prentice Hall, 2002. [51] Mary Kathryn Thompson, John M. Thompson, “ANSYS Mechanical APDL for Finite Element Analysis”, Elsevier Inc., pp 1-6, 2017. [52] Jørgen Arendt Jensen, “Users’ guide for the Field II program”, Technical University of Denmark, Release 3.24, May 12, 2014. 85 [53] S. McAleavey, “Field II Tutorial”, University of Rochester Medical Center, October 27, 2014. [54] J.A. Jensen, “Field: A Program for Simulating Ultrasound Systems”,10th Nordic-Baltic Conference on Biomedical Imaging Published in Medical & Biological Engineering & Computing, pp. 351-353, Volume 34, Supplement 1, Part 1, 1996. [55] K. R. Nightingale, M. L. Palmeri, R. W. Nightingale, and G. E. Trahey, “On the feasibility of remote palpation using acoustic radiation force,” J. Acoust. Soc. Am., vol. 110, pp. 625–634, July 2001. [56] W. F. Walker, F. J. Fernandez, and L. A. Negron, “A method of imaging viscoelastic parameters with acoustic radiation force,” Phys. Med. Biol., vol. 45, no. 6, pp. 1437–1447, June 2000. [57] S. Foster, P. Embree, and W. O’Brien, “Flow velocity profile via time-domain correlation: Error analysis and computer simulation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 37, pp. 164– 175, May 1990. [58] B. Geiman, L. Bohs, M. Anderson, S. Breit, and G. Trahey, “A comparison of algorithms for tracking sub-pixel speckle motion.” in Proc. IEEE Ultrasonics Symp., Oct. 5–8, vol. 2, pp. 1239–1242, 1997. [59] P. G. de Jong, T. Arts, A. P. Hoks, and R. S. Reneman, “Determination of tissue motion velocity by correlation interpolation of pulsed ultrasonic echo signals,” Ultrason. Imaging, vol. 12, no. 2, pp. 84–98, Apr. 1990. [60] B. J. Geiman, L. N. Bohs, M. E. Anderson, S. M. Breit, and G. E. Trahey, “A novel interpolation strategy for estimating subsample speckle motion,” Phys. Med. Biol., vol. 45, no. 6, pp. 1541–1552, Jun. 2000. [61] S. Srinivasan, R. Righetti, and J. Ophir, “Tradeoffs between the axial resolution and the signal-to-noise ratio in elastography,” Ultrasound Med. Biol., vol. 29, no. 6, pp. 847–866, Jun. 2003. [62] F. Walker and E. Trahey, “A fundamental limit on delay estimation using partially correlated speckle signals,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 42, no. 2, pp. 301–308, Mar. 1995. [63] Zhennan Yan, Shaoting Zhang, S. Kaisar Alam, Dimitris N. Metaxas, Brian S. Garra, Ernest J. Feleppa, “Modulus Reconstruction from Prostate Ultrasound Images using Finite Element Modeling”, Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy, Proc. of SPIE Vol. 8320, 832016, 2012. 86 [64] Zhang, S., Gu, L., Huang, P., and Xu, J., “Real-Time Simulation of Deformable Soft Tissue Based on Mass-Spring and Medial Representation,” in Computer Vision for Biomedical Image Applications, 419–426 ,2005. [65] Zhang, S., Wang, X., Metaxas, D. N., Chen, T., and Axel, L., “LV Surface Reconstruction from Sparse tMRI using Laplacian Surface Deformation and Optimization,” in International Symposium on Biomedical Imaging, 698–701, 2009. [66] Zhang, S., Huang, J., and Metaxas, D. N., “Robust mesh editing using Laplacian coordinates,” Graphical Models 73, 10–19, 2011. [67] Wang, X., Chen, T., Zhang, S., Metaxas, D. N., and Axel, L., “LV Motion and Strain Computation from tMRI Based on Meshless Deformable Models,” in Medical Image Computing and Computer Assisted Intervention, 636–644, 2008. [68] Sen Zhong, Wei Xia, and Zishu He, “Approximate maximum likelihood time differences estimation in the presence of frequency and phase consistence errors,” in IEEE International Symposium on Signal Processing and Information Technology, 305-308, 12-15 Dec., 2013. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1342 | |
| dc.description | Supervised by Prof. Dr. Md. Fokhrul Islam, Professor, Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Boardbazar, Gazipur. Bangladesh. | en_US |
| dc.description.abstract | In recent medical diagnosis, ultrasound strain imaging or elastography has been established as a useful technique to determine cancerous or abnormal tissues because a malignant tissue is stiffer than benign (normal) tissue. In elastography, the tissue strain is basically estimated from the gradient of tissue displacements and displacements of tissue are estimated from the time delays of gated pre- and post-compression echo signals. Most of the algorithms that are used to find the tissue displacement in elastography are one dimensional that means the displacement is measured only for axial direction which does not provide us with other directional deformation. Moreover, the direct strain imaging techniques are very computational costly that means it took lots of time to compute the data for imaging. To overcome those above discussed problem, this thesis introduces 2-D cross-correlation algorithm to compute time delay which could give us a more realistic reminiscence. In addition to that, the work flow of the strain imaging has been modified to make the imaging less computational costly. Also, to substantiate the above claim MATLAB tic toc function is used to determine each step simulation time of the modified work flow. In this thesis, the presented imaging technique is based on signal correlation. Correlation of signal is typically a measure of similarity of two signals as a function of the displacement of one relative to the other. At first, a 2D finite element tissue mimicking phantom model based on real tissue properties using ANSYS software having a tumor inclusion surrounded by soft tissue is developed. Then, the synthetic tissue was simulated for different inclusion tissue properties for a given displacement. After that, pre- and post-compression data was exported from the ANSYS model. Then, ultrasound simulation (using FIELD II) was done to generate pre- and postcompression RF signal for a more realistic simulation. After that, 2D cross correlation MATLAB function was used to estimate the time delay between pre- and post-compression RF signals. Using MATLAB surf tool, the estimated correlation coefficient was mapped. The map shows a promising result to distinguish between a normal and abnormal tissue. Also, the proposed algorithm offers less computation cost in terms of simulation time with a value of 222.072322 seconds in contrast with the simulation time of the conventional strain imaging having a value more than 222.093015 seconds. So, by applying the above imaging algorithm and procedure to a real world 3-D scenario, we could get a more sophisticated imaging technique which is also computation costly. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh | en_US |
| dc.title | Development of a Realistic Tissue Mimicking Phantom Using Finite Element Method | en_US |
| dc.type | Thesis | en_US |
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2021 [12]