Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues
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Other documents of the author: Lago, M.A.; Rupérez, M.J.; Martínez-Martínez, F.; Martínez-Sanchis, S.; Bakic, P.R.; Monserrat, C.
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comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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http://dx.doi.org/10.1016/j.eswa.2015.05.058 |
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Title
Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissuesAuthor (s)
Date
2015Publisher
ElsevierISSN
0957-4174Bibliographic citation
LAGO, M. A., et al. Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues. Expert Systems with Applications, 2015, vol. 42, no 21, p. 7942-7950.Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S0957417415003942Subject
Abstract
This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on ... [+]
This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similarity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work. [-]
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Expert Systems with Applications Volume 42, Issue 21, 30 November 2015, Pages 7942–7950Rights
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