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.

中文翻译：

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通过从原始光谱OCT数据进行深度学习来估计针尖力。

目的

对于诸如活检或近距离放射治疗的应用，针的放置是具有挑战性的问题。尖端力感测可以为组织内部的针头导航提供有价值的反馈。为此，可以将光纤传感器直接集成到针尖中。光学相干断层扫描（OCT）可用于对组织成像。在这里，我们研究如何校准OCT来感应力，例如在自动机针放置期间。

方法

我们调查了使用原始光谱OCT数据而不进行典型图像重建是否可以改善基于深度学习的光信号和力之间的校准。为此，我们考虑采用卷积神经网络（CNN）校准的，具有新的，更坚固的设计的三种不同的针。我们比较训练原始CNT与原始OCT信号和重建的深度剖面的CNN。

结果

我们发现，使用原始数据作为最大的CNN模型的输入要优于使用重建数据，其平均绝对误差为5.81 mN，而平均误差为8.04 mN。

结论

我们发现，使用原始光谱OCT数据进行的深度学习可以改善力估计任务的学习。我们的针头设计和校准方法构成了一种非常精确的光纤传感器，用于测量针尖处的力。