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Portrait of Predrag Bakic. Photo

Predrag Bakic

Associate Professor

Portrait of Predrag Bakic. Photo

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues

Author

  • M. A. Lago
  • M. J. Rupérez
  • F. Martínez-Martínez
  • S. Martínez-Sanchis
  • P. R. Bakic
  • C. Monserrat

Summary, in English

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.

Publishing year

2015-07-06

Language

English

Pages

7942-7950

Publication/Series

Expert Systems with Applications

Volume

42

Issue

21

Document type

Journal article

Publisher

Elsevier

Topic

  • Medical Image Processing
  • Cancer and Oncology

Keywords

  • Breast biomechanical modeling
  • Genetic heuristics
  • In-vivo tissue characterization
  • Parameter estimation

Status

Published

ISBN/ISSN/Other

  • ISSN: 0957-4174