Fiber Bragg grating and artificial intelligence fusion for shape self-sensing puncture needle

LI Tianliang; SONG Zhenzhen; CHEN Fayin; SU Yifei; TAN Yuegang

doi:10.37188/OPE.20233102.0160

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Fiber Bragg grating and artificial intelligence fusion for shape self-sensing puncture needle

Modern Applied Optics | 更新时间：2023-02-08

- Fiber Bragg grating and artificial intelligence fusion for shape self-sensing puncture needle
- Optics and Precision Engineering Vol. 31, Issue 2, Pages: 160-167(2023)
- 作者机构：
  
  武汉理工大学机电工程学院，湖北武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233102.0160
  CLC： TN253
- Received：08 June 2022，
  
  Revised：01 August 2022，
  
  Published：25 January 2023
- 稿件说明：
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李天梁,宋珍珍,陈发银等.光纤光栅与人工智能融合的形状自感知穿刺针[J].光学精密工程,2023,31(02):160-167.

LI Tianliang,SONG Zhenzhen,CHEN Fayin,et al.Fiber Bragg grating and artificial intelligence fusion for shape self-sensing puncture needle[J].Optics and Precision Engineering,2023,31(02):160-167.
李天梁,宋珍珍,陈发银等.光纤光栅与人工智能融合的形状自感知穿刺针[J].光学精密工程,2023,31(02):160-167. DOI： 10.37188/OPE.20233102.0160.

LI Tianliang,SONG Zhenzhen,CHEN Fayin,et al.Fiber Bragg grating and artificial intelligence fusion for shape self-sensing puncture needle[J].Optics and Precision Engineering,2023,31(02):160-167. DOI： 10.37188/OPE.20233102.0160.

摘要

手术机器人可以通过术前路径规划避免重要组织受到损伤，且具有较高的操作精度，因而被广泛应用于穿刺活检手术之中。目前手术机器人在穿刺针形状信息准确实时获取方面存在不足，无法实现穿刺过程中自主避障以及精准的靶点操作。因此研究穿刺针形状感知方法，可为手术机器人自主/主从穿刺提供形状信息反馈，对于提高穿刺手术的安全性与准确性有着重大意义。鉴于此，提出了一种融合分布式光纤光栅传感技术和人工智能的形状自感知穿刺针。利用机器学习算法，对静态标定实验获取的不同弯曲状态下光纤光栅的中心波长数据与形状数据进行训练，得到了光纤光栅中心波长漂移量与形状函数及弯曲方向角之间的神经网络模型，进一步调用该神经网络模型，实现穿刺针的三维形状重构；测试结果表明，穿刺针针体的形状重构最大误差值为0.90 mm，弯曲方向角的最大误差为5.03°。在动态性能验证实验中，形状重构最大误差值为0.84 mm，弯曲方向角的最大误差为1.02°，进一步验证了模型的可靠性。因此所提出的形状自感知穿刺针能够精准实现形状信息的实时获取，从而在手术机器人自主穿刺和主从操作过程中针体形状感知与调控方面具有广阔的应用前景。

Abstract

Surgical robots can avoid damage to important tissues through preoperative path planning before surgery. As they have high operational accuracy， they are widely used in needle biopsy operations. However， surgical robots cannot currently achieve accurate and real-time acquisition of puncture needle shape information. This problem makes it difficult for them to autonomously avoid obstacles and prevents the needle from precisely reaching its target in order to carry the puncture procedure out. Therefore， studying a shape perception method for needles to provide shape information feedback for autonomous/master-slave punctures by surgical robots， is important for improving the safety and accuracy of puncture surgery. Therefore， a shape self-sensing puncture needle that integrates distributed optical fiber sensing technology and artificial intelligence was developed in this study. A neural network model algorithm was trained using training data comprising the center wavelength data and shape data of the fiber Bragg grating under different bending states， both of which were obtained from a static calibration experiment. Then， the model was used to realize the three-dimensional shape reconstruction of the puncture needle. Experimental results indicate that the maximum error of the needle shape reconstruction is 0.90 mm， and that the maximum error of the bending directional angle is 5.03°. As regards the dynamic experiment， the maximum error of the shape reconstruction is 0.84 mm， and the maximum error of the bending directional angle is 1.02°； the reliability of the model is thus validated. Thus， the proposed shape self-sensing puncture needle can accurately realize real-time acquisition of the shape， and it has broad application prospects in needle shape perception and regulation during autonomous puncture and master-slave operations by surgical robots.

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