  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.
