Combining ultrasound-based elasticity estimation and FE models to predict 3D target displacement

Area of Science:

• Medical Imaging
• Biomechanical Engineering
• Surgical Robotics

Background:

• Minimally invasive surgery involves needle insertion, leading to tissue deformation and potential surgical tool misplacement.
• Accurate prediction of target displacement is crucial for enhancing surgical precision and patient outcomes.

Purpose of the Study:

• To develop and validate a 3D technique for predicting target displacement during minimally invasive procedures.
• To combine ultrasound-based acoustic radiation force impulse (ARFI) for soft-tissue elasticity estimation with finite element (FE) models.

Main Methods:

• Developed 3D finite element (FE) models of tissue phantoms with embedded targets.
• Utilized ultrasound-based acoustic radiation force impulse (ARFI) to estimate soft-tissue elasticity.
• Acquired 3D ultrasound images during loading/unloading to calculate target displacement and validate FE model predictions.

Main Results:

• The combined ARFI and FE model approach accurately predicted target displacement in 3D phantoms.
• The maximum absolute error in predicted target displacement was 1.39mm, below the smallest detectable tumor size in breast tissue.
• This demonstrates the feasibility of the technique for real-time surgical guidance.

Conclusions:

• The integration of ARFI-based elasticity estimation and 3D FE modeling provides a robust method for predicting target displacement.
• This technique holds significant potential for developing patient-specific surgical plans and improving the safety of minimally invasive interventions.
• Future work could involve real-time implementation for intraoperative guidance.