A Calibration Approach for Elasticity Estimation with Medical Tools

TL;DR

This paper introduces a calibration method for medical tools to accurately estimate tissue elasticity during minimally invasive procedures, accounting for tool geometry and temperature effects to improve tissue characterization.

Contribution

It presents an experimental setup for calibrating medical tools using a neural network model that incorporates temperature-dependent tissue elasticity estimation.

Findings

Gelatin elasticity varies significantly with temperature.

Neural network models improve elasticity estimation accuracy.

Elasticity values ranged from 10 to 40 kPa depending on conditions.

Abstract

Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature.