Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture

TL;DR

This paper presents a novel image-based deep learning method using OCT fiber data to accurately estimate needle tip forces during minimally invasive procedures, enhancing tissue interaction understanding.

Contribution

It introduces a fused convGRU-CNN model leveraging optical fiber imaging for precise force estimation, adaptable to different needle materials and validated in ex-vivo scenarios.

Findings

Achieved mean absolute error of 1.76 mN in force estimation.

Model outperforms existing methods in accuracy.

Validated approach in ex-vivo prostate needle insertion.

Abstract

Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy. During soft tissue needle insertion, forces acting at the needle tip cause tissue deformation and needle deflection. Accurate needle tip force measurement provides information on needle-tissue interaction and helps detecting and compensating potential misplacement. For this purpose we introduce an image-based needle tip force estimation method using an optical fiber imaging the deformation of an epoxy layer below the needle tip over time. For calibration and force estimation, we introduce a novel deep learning-based fused convolutional GRU-CNN model which effectively exploits the spatio-temporal data structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1.76 +- 1.5 mN with a cross-correlation coefficient of 0.9996, clearly outperforming other methods. We test needles with different materials to demonstrate that the approach can be adapted for different sensitivities and force ranges. Furthermore, we validate our approach in an ex-vivo prostate needle insertion scenario.