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Published on: September 2, 2025
Target motion predictions for pre-operative planning during needle-based interventions.
Jorn op den Buijs1, Momen Abayazid, Chris L de Korte
1MIRA–Institute of Biomedical Technology and Technical Medicine, Control Engineering Group, University of Twente, The Netherlands. s.misra@utwente.nl
Summary
Accurate breast biopsy needle placement can be improved using finite element (FE) models to predict tissue motion during procedures. These models, validated with ultrasound elastography, enhance pre-operative planning for better lesion targeting.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Computational Mechanics
Background:
- Breast biopsies involve needle insertion, causing tissue displacement that complicates accurate targeting.
- Pre-operative prediction of tissue motion is crucial for precise needle placement during biopsies.
Purpose of the Study:
- To develop and validate finite element (FE) models for predicting breast tissue motion during needle indentation.
- To assess the accuracy of FE models using ultrasound elastography measurements on breast tissue phantoms.
Main Methods:
- Ultrasound elastography was used to non-invasively measure elastic properties of breast tissue phantoms.
- Finite element (FE) models were created using these properties to simulate needle indentation.
- Experimental measurements tracked target displacement during indentation to validate FE model predictions.
Main Results:
- FE model predictions showed good agreement with experimental measurements, with maximum errors of 12% and 3% for different phantom support conditions.
- Parameter sensitivity analysis revealed that increased skin elastic modulus and shallower target depth enhance target motion.
- Accurate prediction of tissue indentation is achievable with known geometry, boundary conditions, and elastic properties.
Conclusions:
- Finite element (FE) models, combined with pre-operative data, can accurately predict breast tissue indentation during biopsies.
- Integrating FE models with robotic systems can significantly improve lesion targeting accuracy in breast biopsies.
