A Force-Sensor-Less Approach for Rapid Young's Modulus Identification of Heterogeneous Soft Tissue

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Mechanics

Background:

• Accurate tissue elastic parameter identification is vital for biomechanical modeling in surgical guidance.
• Heterogeneous soft tissue properties pose challenges for precise measurement during procedures like hepatic venous injections.

Purpose of the Study:

• To develop a method for acquiring the absolute Young's modulus of heterogeneous soft tissues during endoscopic surgery using 2D ultrasound images.
• To enable precise biomechanical modeling for improved surgical guidance.

Main Methods:

• A force-sensor-less approach using a precalibrated soft patch to estimate external forces from ultrasound images.
• A Kriging-based inverse algorithm to identify relative Young's modulus (RYM) between tissue inclusions and background.
• A direct method to determine background Young's modulus (BYM) using estimated forces and RYM, validated with 2D plane strain approximation and a trained Kriging model for 3D mapping.

Main Results:

• The Kriging-based inverse algorithm demonstrated high efficiency and robustness in RYM identification through simulations.
• Physical experiments on three phantoms showed identification errors for both BYM and RYM below 15%.
• The proposed methodology achieved satisfactory accuracy and computational efficiency in both simulated and physical experiments.

Conclusions:

• The developed methodology for Young's modulus identification is feasible for heterogeneous soft tissues.
• The approach provides accurate and computationally efficient measurements essential for biomechanical modeling in surgical guidance.
• This technique enhances the potential for real-time elastic parameter assessment during endoscopic procedures.