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Finite element modelling and validation for breast cancer detection using digital image elasto-tomography

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Abstract

Finite element (FE) models are increasingly used to validate experimental data in breast cancer. This research constructed a biomechanical FE model for breast shaped phantoms used to develop and validate a mechanical vibration based screening system. Such models do not currently exist but would enhance development of this screening technology. Three phantoms were modelled: healthy, with 10 and 20 mm inclusions. The overall goal was to create models with enough accuracy to replace experimental phantoms in providing data to optimize diagnostic algorithms for digital image-based elasto-tomography (DIET) screening technologies. FE model results were validating against experimental DIET phantom data for over 4000 collected points on each model and phantom using cross-correlation coefficients between experimental simulated data and direct comparison. Results showed good to strong correlation ranging from 0.7 to 1.0 in all cases with over 90% having a value over 0.9. Magnitudes for each frame of the dynamic response also matched well, indicating that the material properties and geometry were accurate enough to provide this level of correlation. These results justify the use of FE model generated data for in silico diagnostic algorithm development testing. The overall modelling and validation approach is not overly complex, and thus generalizable to similar problems using mechanical properties of silicone phantoms, and might be extensible to human cases with further work.

Validate that dynamic displacements show that the model can be used in place of phantoms for rapid development of diagnostic algorithms that use surface motion to detect underlying mechanical properties.

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Authors and Affiliations

  1. University of Canterbury, 20 Kirkwood Ave, Upper Riccarton, Christchurch, 8041, New Zealand

    Hina M. Ismail, Chris G. Pretty & J. Geoffrey Chase

  2. Tiro Medical Limited, Building Dg7, Dovedale Village, Dovedale Avenue, Ilam, Christchurch, 8041, New Zealand

    Matthew K. Signal & Marcus Haggers

Authors
  1. Hina M. Ismail
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  2. Chris G. Pretty
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  3. Matthew K. Signal
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  4. Marcus Haggers
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  5. J. Geoffrey Chase
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Correspondence to Hina M. Ismail.

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Ismail, H.M., Pretty, C.G., Signal, M.K. et al. Finite element modelling and validation for breast cancer detection using digital image elasto-tomography. Med Biol Eng Comput 56, 1715–1729 (2018). https://doi.org/10.1007/s11517-018-1804-5

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  • DOI: https://doi.org/10.1007/s11517-018-1804-5

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