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

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Medical Imaging

Background:

• Finite element (FE) models are crucial for validating experimental data in breast cancer research.
• Existing biomechanical models are insufficient for developing novel screening technologies.

Purpose of the Study:

• To construct an accurate biomechanical FE model for breast phantoms.
• To validate the FE model against experimental data for optimizing digital image-based elasto-tomography (DIET) screening algorithms.

Main Methods:

• Developed FE models of three breast phantoms (healthy, 10mm inclusion, 20mm inclusion).
• Validated FE model results against experimental DIET phantom data using cross-correlation coefficients and direct comparison.
• Analyzed dynamic displacement magnitudes for model accuracy.

Main Results:

• Achieved good to strong correlation (0.7-1.0) between FE model simulations and experimental data.
• Over 90% of data points showed a correlation coefficient above 0.9.
• Matched dynamic response magnitudes, confirming accurate material properties and geometry.

Conclusions:

• FE model-generated data can reliably replace experimental phantoms for in silico diagnostic algorithm development.
• The modeling approach is generalizable to silicone phantoms and potentially extensible to human applications.
• Validated FE models accelerate the development of surface motion-based breast cancer screening technologies.