Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy.

Area of Science:

• Biomedical Engineering
• Medical Devices
• Diagnostic Technologies

Background:

• Histological analysis relies on accurate tissue sampling during biopsies.
• Current biopsy methods often lack real-time information about the instrument tip's location.
• This limitation can compromise the diagnostic value of tissue samples.

Purpose of the Study:

• To evaluate the resolution of a novel bioimpedance-based tissue identification system for biopsy needles.
• To assess the impact of tissue heterogeneity on bioimpedance measurements and classification.
• To determine the in vivo feasibility and accuracy of this technology for clinical application.

Main Methods:

• Utilized finite element method (FEM) simulations with a 3D model to analyze bioimpedance measurements.
• Collected in vivo data from a porcine model using a moving biopsy needle across various tissues (fat, muscle, blood, liver, spleen).
• Developed and validated a tissue classifier algorithm based on the collected in vivo bioimpedance data.

Main Results:

• FEM simulations demonstrated the detectability of very small targets and accurate measurement of targets 2x2x2 mm³ and larger.
• The in vivo tissue classifier achieved a high overall accuracy of approximately 94% for differentiating between various tissue types.
• The study confirmed the feasibility of real-time, local tissue classification directly at the biopsy needle tip.

Conclusions:

• Bioimpedance-based tissue classification is a feasible and accurate method for in vivo applications.
• This novel technology has significant potential to improve the precision and reliability of clinical biopsy procedures.
• Real-time tissue identification can enhance diagnostic accuracy by ensuring appropriate tissue is sampled.