پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 427 |
| تعداد مقالات | 5,569 |
| تعداد مشاهده مقاله | 6,763,807 |
| تعداد دریافت فایل اصل مقاله | 5,728,377 |
بهینه‌سازی ضرایب مدل ساختاری هایپرالاستیک بافت نرم با استفاده از الگوریتم رقابت استعماری و داده‌های تجربی | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 4، دوره 49، شماره 2، مرداد 1396، صفحه 269-278 اصل مقاله (901.11 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2016.723 | ||
| نویسندگان | ||
| علی امارلو؛ میلاد کشاورز؛ افسانه مجری* | ||
| دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه نصیر الدین طوسی، تهران، ایران | ||
| چکیده | ||
| در این مطالعه، هدف شناسایی مدل ساختاری بافت نرم می‌باشد. به این منظور از یک دستگاه رباتیک مجهز به حس‌گر لامسه‌ای برای اعمال نیرو به بافت نرم استفاده شده است. بافت سینه‌ی فرد توسط دستگاه مورد معاینه قرار گرفته است و خروجی تنش و کرنش در طی دو مرحله‌ی بارگذاری و باربرداری استخراج شده است. مجموعه‌ی داده‌های دقیق تجربی برای تولید مدل ساختاری از رفتار هایپرالاستیک بافت مورد استفاده قرار می‌گیرد. هشت مدل ساختاری هایپرالاستیک برای تطبیق با داده‌های تجربی تنش-کرنش بافت نرم معرفی گردیده است. به منظور محاسبه‌ی بهینه‌ی پارامترهای مدل‌ها و همچنین انتخاب مدل بهینه، یک تابع هدف تعریف شده است که اختلاف میان داده‌های تجربی و مدل-سازی می‌باشد. برای کمینه کردن مقدار تابع هدف از یک الگوریتم بهینه‌سازی قدرتمند به نام الگوریتم رقابت استعماری استفاده شده است. مدل ساختاری بدست آمده یک مدل قابل اعتماد و دارای کمترین تفاوت نسبت به رفتار طبیعی بافت است. نوآوری پژوهش حاضر در بدست آوردن یک مدل ریاضی واقعی دارای دقت بالا از بافت نرم با استفاده از داده‌های تجربی می‌باشد. از این مدل می‌توان برای پیش‌بینی رفتار مکانیکی بافت تحت معاینه‌ی پزشک و طراحی شبیه‌ساز جراحی بافت سینه برای کمک و آموزش به جراحان استفاده نمود. از دیگر برتری‌های مهم مدل تولید شده این است که با دسته‌بندی پارامترهای بدست آمده از نمونه‌های بیمار و سالم، می‌توان محدوده‌ای مخصوص به پارامترهای بافت سالم و همچنین بافت بیمار بدست آورد. این امر کمک مؤثری در راستای تشخیص بیماری بدون استفاده از تکنیک‌های تصویربرداری و یا نمونه‌برداری از بافت است. | ||
| کلیدواژه‌ها | ||
| بافت نرم؛ بهینه‌سازی؛ الگوریتم رقابت استعماری؛ مدل ساختاری هایپرالاستیک؛ حس لامسه‌ی مصنوعی | ||
| عنوان مقاله [English] | ||
| Optimization of Hyperelastic Constitutive Model Coefficients for Soft Tissue by Imperialistic Competitive Algorithm Based on Experimental Data | ||
| نویسندگان [English] | ||
| A. Amarloo؛ M. Keshavarz؛ A. Mojra | ||
| Mechanical Engineering Department, K. N. Toosi University of Technology, Tehran Iran | ||
| چکیده [English] | ||
| The main target of this study is identification of the constitutive model of a soft tissue. For such a purpose a robotic tactile device (Robo-Tac-BMI) was used for breast tissue examinations and stress versus strain was collected for every test point during loading and unloading processes. Utilizing accurate experimental dataset for mechanical modeling of the tissue in conjunction with an optimization algorithm provides a reliable constitutive model of tissue’s mechanical behavior. Eight major hyperelastic models were adapted to the stress-strain data to find the most compatible constitutive equation applicable to the soft tissue mechanical behavior. For this purpose, a new optimization algorithm called Imperialist Competitive Algorithm (ICA) which is based on social and political strategy was used. The novelty of the present study is producing a realistic mathematical model with high accuracy of the soft tissue based on experimental data. The achieved hyperelastic model can be used for prediction of mechanical behavior of the breast tissue in surgery simulation for assistance and educational purposes. Other application of this model is clustering of healthy and cancerous tissue which facilitates the surgeon’s task in the diagnosis procedure. This application also makes the diagnosis procedure almost independent of using imaging techniques or performing biopsies. This model is useful in distinguishing cases where the soft tissue has altered from normal situation like tumors and cancer attacks. | ||
| کلیدواژه‌ها [English] | ||
| Soft tissue, optimization, Imperialistic Competition Algorithm, Hyperelastic Constitutive Model, Artificial Tactile Sensing | ||
|
سایر فایل های مرتبط با مقاله
|
||
| مراجع | ||
|
[1] A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D.Forman, Global cancer statistics, CA: a cancer journal for clinicians, 61(2) (2011) 69-90. [2] M.E. Akbari, Interview with IRNA (Islamic Republic News Agency), in, 2014. [3] WHO, The top 10 causes of death Fact sheet N 310, in,2014. [4] WHO, World Cancer Report 2014, in, 2014. [5] A.S. Khalil, R.C. Chan, A.H. Chau, B.E. Bouma,M.R.K. Mofrad, Tissue elasticity estimation with optical coherence elastography: toward mechanical characterization of in vivo soft tissue, Annals of biomedical engineering, 33(11) (2005) 1631-1639. [6] K. Parker, S. Huang, R. Musulin, R. Lerner, Tissue response to mechanical vibrations for "sonoelasticity imaging", Ultrasound in medicine & biology, 16(3)(1990) 241-246. [7] J. Kim, M.A. Srinivasan, Characterization of viscoelastic soft tissue properties from in vivo animal experiments and inverse FE parameter estimation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2005, pp. 599-606. [8] C. Lucarotti, C.M. Oddo, N. Vitiello, M.C. Carrozza,Synthetic and bio-artificial tactile sensing: A review,Sensors, 13(2) (2013) 1435-1466. [9] C.K. Raj, A. Revanth, K. Karthick, Tactile Sensing System Using Artificial Neural Networks, Journal of NanoScience and NanoTechnology, 2(1) (2014) 670-673. [10] A.M. Okamura, C. Simone, M.D. O'leary, Force modeling for needle insertion into soft tissue, IEEE transactions on biomedical engineering, 51(10) (2004)1707-1716. [11] S.M. Hosseini, S.M.T. Kashani, S. Najarian, F. Panahi,S.M.M. Naeini, A. Mojra, A medical tactile sensing instrument for detecting embedded objects, with specific application for breast examination, The international journal of medical robotics and computer assisted surgery, 6(1) (2010) 73-82. [12] A. Mojra, S. Najarian, S. Hosseini, S. Towliat Kashani,F. Panahi, Artificial tactile sensing capability analysis in abnormal mass detection with application in clinical breast examination, in: The World Congress on Engineering, 2011, pp. 2663-2666. [13] H. Mehrabian, A. Samani, An iterative hyperelastic parameters reconstruction for breast cancer assessment,in: Proc. of SPIE Vol, 2008, pp. 69161C-69161. [14] F.A. Duck, Physical properties of tissues: a comprehensive reference book, Academic press, 2013. [15] T.A. Krouskop, T.M. Wheeler, F. Kallel, B.S. Garra, T.Hall, Elastic moduli of breast and prostate tissues under compression, Ultrasonic imaging, 20(4) (1998) 260-274. [16] A.S. Khalil, B.E. Bouma, M.R.K. Mofrad, A combined FEM/genetic algorithm for vascular soft tissue elasticity estimation, Cardiovascular Engineering, 6(3) (2006) 93-102. [17] A. Karimi, M. Navidbakhsh, B. Beigzadeh, A viscohyperelastic constitutive approach for modeling polyvinyl alcohol sponge, Tissue and cell, 46(1) (2014)97-102. [18] H. Khajehsaeid, J. Arghavani, R. Naghdabadi, A hyperelastic constitutive model for rubber-like materials,European Journal of Mechanics-A/Solids, 38 (2013)144-151. [19] M. Sasso, G. Palmieri, G. Chiappini, D. Amodio, Characterization of hyperelastic rubber-like materials by biaxial and uniaxial stretching tests based on optical methods, Polymer Testing, 27(8) (2008) 995-1004. [20] Y. Fu, C. Chui, C. Teo, E. Kobayashi, Elasticity imaging of biological soft tissue using a combined finite element and non-linear optimization method, Inverse Problems in Science and Engineering, 23(2) (2015) 179-196. [21] Y. Zhang, Y. Mukaibo, T. Maeno, A multi-purpose tactile sensor inspired by human finger for texture and tissue stiffness detection, in: Robotics and Biomimetics,2006. ROBIO'06. IEEE International Conference on,IEEE, 2006, pp. 159-164. [22] G. Zhang, A. Wittek, G. Joldes, X. Jin, K. Miller, A threedimensional nonlinear meshfree algorithm for simulating mechanical responses of soft tissue, Engineering Analysis with Boundary Elements, 42 (2014) 60-66. [23] P. Boonvisut, M.C. Cavusoglu, Estimation of soft tissue mechanical parameters from robotic manipulation data, IEEE/ASME Transactions on Mechatronics, 18(5) (2013) 1602-1611. [24] M. Mooney, A theory of large elastic deformation,Journal of applied physics, 11(9) (1940) 582-592. [25] R. Rivlin, Large elastic deformations of isotropic materials. IV. Further developments of the general theory, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 241(835) (1948) 379-397. [26] M. Pawlikowski, Non-linear approach in viscohyperelastic constitutive modelling of polyurethane nanocomposite, Mechanics of Time-Dependent Materials, 18(1) (2014) 1-20. [27] T. El Sayed, A. Mota, F. Fraternali, M. Ortiz, A variational constitutive model for soft biological tissues,Journal of biomechanics, 41(7) (2008) 1458-1466. [28] E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, in: Evolutionary computation, 2007. CEC 2007. IEEE Congress on, IEEE, 2007, pp.4661-4667. [29] A. Mojra, S. Najarian, S.M. Towliat Kashani, F. Panahi, M. Yaghmaei, A novel haptic robotic viscogram for characterizing the viscoelastic behaviour of breast tissue in clinical examinations, The International Journal of Medical Robotics and Computer Assisted Surgery, 7(3)(2011) 282-292. [30] A. Mojra, S. Najarian, S.M. Towliat Kashani, F. Panahi,M.A. Tehrani, A novel robotic tactile mass detector with application in clinical breast examination, Minimally Invasive Therapy & Allied Technologies, 21(3) (2012)210-221. [31] A. Mojra, S. Najarian, S.T. Kashani, F. Panahi, A novel tactile-guided detection and three-dimensional localization of clinically significant breast masses,Journal of medical engineering & technology, 36(1)(2012) 8-16. [32] X. Ning, Q. Zhu, Y. Lanir, S.S. Margulies, A transversely isotropic viscoelastic constitutive equation for brainstem undergoing finite deformation, Journal of biomechanical engineering, 128(6) (2006) 925-933. [33] E. Atashpaz Gargari, F. Hashemzadeh, R. Rajabioun, C.Lucas, Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process, International Journal of Intelligent Computing and Cybernetics, 1(3) (2008) 337-355. [34] A.R. Taheri, Human Socio-political Evolution and Imperialistic Competitive Algorithm Application in Optimize the Huge-Combinatorial problem, in:International Conference of Iranian Operations Research Society, Iran, 2009, pp. 76-79. [35] R. Rajabioun, E. Atashpaz-Gargari, C. Lucas, Colonial competitive algorithm as a tool for Nash equilibrium point achievement, Computational science and its applications-iccsa 2008, (2008) 680-695. [36] M.M. Attard, G.W. Hunt, Hyperelastic constitutive modeling under finite strain, International Journal of Solids and Structures, 41(18) (2004) 5327-5350. [37] D. Veronda, R. Westmann, Mechanical characterization of skin-finite deformations, Journal of iomechanics,3(1) (1970) 111IN9123-122124. [38] R.W. Ogden, Non-linear elastic deformations, Courier Corporation, 1997. [39] P. Boulanger, M. Hayes, Finite-amplitude waves in Mooney-Rivlin and Hadamard materials, in: Topics in finite elasticity, Springer, 2001, pp. 131-167. [40] R.S. Rivlin, D. Saunders, Large elastic deformations of isotropic materials. VII. Experiments on the deformation of rubber, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 243(865) (1951) 251-288. [41] R.W. Ogden, Large deformation isotropic elasticityon the correlation of theory and experiment for incompressible rubberlike solids, in: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, The Royal Society, 1972, pp. 565-584. [42] Y.-c. Fung, Biomechanics, Springer, 1990. [43] O. Yeoh, Some forms of the strain energy function for rubber, Rubber Chemistry and technology, 66(5) (1993)754-771. [44] A. Gent, A new constitutive relation for rubber, Rubber chemistry and technology, 69(1) (1996) 59-61. [45] J. Bergstrom, M. Boyce, Deformation of elastomeric networks: relation between molecular level deformation and classical statistical mechanics models of rubber and classical statistical mechanics models of rubber elasticity, Macromolecules, 34(3) (2001) 614-626. | ||
|
آمار تعداد مشاهده مقاله: 1,853 تعداد دریافت فایل اصل مقاله: 2,126 |
||
