Needle tip force estimation by deep learning from raw spectral OCT data

TL;DR

This paper demonstrates that deep learning models trained on raw spectral OCT data can accurately estimate needle tip forces, improving calibration for needle navigation in medical procedures.

Contribution

The study introduces a novel approach of using raw spectral OCT data with CNNs for force calibration, outperforming traditional reconstructed data methods.

Findings

Raw OCT data improves force estimation accuracy.

CNN models achieve mean absolute error of 5.81 mN.

New needle design enhances force sensing capabilities.

Abstract

Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g. during robotic needle placement. Methods. We investigate whether using raw spectral OCT data without a typical image reconstruction can improve a deep learning-based calibration between optical signal and forces. For this purpose, we consider three different needles with a new, more robust design which are calibrated using convolutional neural networks (CNNs). We compare training the CNNs with the raw OCT signal and the reconstructed depth profiles. Results. We find that using raw data as an input for the largest CNN model outperforms the use of reconstructed data with a mean absolute error of 5.81 mN compared to 8.04 mN. Conclusions. We find that deep learning with raw spectral OCT data can improve learning for the task of force estimation. Our needle design and calibration approach constitute a very accurate fiber-optical sensor for measuring forces at the needle tip.