Detecting stages of needle penetration into tissues through force estimation at needle tip using fiber Bragg grating sensors

Kumar, Saurabh; Shrikanth, V; Bharadwaj, Aprameya; Asokan, S.; Bobji, M. S.

doi:10.1117/1.jbo.21.12.127009

Cited by 17 publications

(18 citation statements)

References 28 publications

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“…They determined a mean force of 13.5 ± 3.7 N [5]. Research groups around Abolhassani, Okamura, Bao, Jiang, Frick and Butz examined single-needle insertion forces for different tissue types and process relevant parameters affecting the measurement values [6][7][8][9][10][11][12]. Typical setups consisted of 1 or 2 DOF testing machines to control insertion, and 6 DOF electronic force transducers [6,7,9,10].…”

Section: State Of the Artmentioning

confidence: 99%

Design of a force-measuring setup for colorectal compression anastomosis and first ex-vivo results

et al. 2021

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Purpose The introduction of novel endoscopic instruments is essential to reduce trauma in visceral surgery. However, endoscopic device development is hampered by challenges in respecting the dimensional restrictions, due to the narrow access route, and by achieving adequate force transmission. As the overall goal of our research is the development of a patient adaptable, endoscopic anastomosis manipulator, biomechanical and size-related characterization of gastrointestinal organs are needed to determine technical requirements and thresholds to define functional design and load-compatible dimensioning of devices. Methods We built an experimental setup to measure colon tissue compression piercing forces. We tested 54 parameter sets, including variations of three tissue fixation configurations, three piercing body configurations (four, eight, twelve spikes) and insertion trajectories of constant velocities (5 mms−1, 10 mms−1,15 mms−1) and constant accelerations (5 mms−2, 10 mms−2, 15 mms−2) each in 5 samples. Furthermore, anatomical parameters (lumen diameter, tissue thickness) were recorded. Results There was no statistically significant difference in insertion forces neither between the trajectory groups, nor for variation of tissue fixation configurations. However, we observed a statistically significant increase in insertion forces for increasing number of spikes. The maximum mean peak forces for four, eight and twelve spikes were 6.4 ± 1.5 N, 13.6 ± 1.4 N and 21.7 ± 5.8 N, respectively. The 5th percentile of specimen lumen diameters and pierced tissue thickness were 24.1 mm and 2.8 mm, and the 95th percentiles 40.1 mm and 4.8 mm, respectively. Conclusion The setup enabled reliable biomechanical characterization of colon material, on the base of which design specifications for an endoscopic anastomosis device were derived. The axial implant closure unit must enable axial force transmission of at least 28 N (22 ± 6 N). Implant and applicator diameters must cover a range between 24 and 40 mm, and the implant gap, compressing anastomosed tissue, between 2 and 5 mm.

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Section: State Of the Artmentioning

confidence: 99%

Design of a force-measuring setup for colorectal compression anastomosis and first ex-vivo results

et al. 2021

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“…Therefore, small-scale fiber-optical force estimation methods have been directly integrated into the needle tip. Several sensor concepts are based on Fabry-Pérot interferometry [1] and fiber Bragg gratings [6]. Here, we consider a setting where optical coherence tomography is B M. Gromniak martin.gromniak@tuhh.de 1 Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany available, e.g., to study tissue deformation [10] or to realize elastography [7].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…For these reasons, fiber optical force sensors have been developed which are often smaller, biocompatible and MRI-compatible [7]. In particular, sensor concepts using Fabry-Pérot interferometry [8,7] or Fiber Bragg Gratings [9,10,11] have been proposed. These methods have shown promising calibration results, however, manufacturing and signal processing can be difficult when different temperature ranges and lateral forces need to be taken into account.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal deep learning models for tip force estimation during needle insertion

et al. 2019

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PurposePrecise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates.MethodsWe describe a fiber-optic needle tip force sensor design using a single OCT fiber for measurement. The fiber images the deformation of an epoxy layer placed below the needle tip which results in a stream of 1D depth profiles. We study different deep learning approaches to facilitate calibration between this spatio-temporal image data and the related forces. In particular, we propose a novel convGRU-CNN architecture for simultaneous spatial and temporal data processing.ResultsThe needle can be adapted to different operating ranges by changing the stiffness of the epoxy layer. Likewise, calibration can be adapted by training the deep learning models. Our novel convGRU-CNN architecture results in the lowest mean absolute error of and a cross-correlation coefficient of 0.9997 and clearly outperforms the other methods. Ex vivo experiments in human prostate tissue demonstrate the needle’s application.ConclusionsOur OCT-based fiber-optic sensor presents a viable alternative for needle tip force estimation. The results indicate that the rich spatio-temporal information included in the stream of images showing the deformation throughout the epoxy layer can be effectively used by deep learning models. Particularly, we demonstrate that the convGRU-CNN architecture performs favorably, making it a promising approach for other spatio-temporal learning problems.Electronic supplementary materialThe online version of this article (10.1007/s11548-019-02006-z) contains supplementary material, which is available to authorized users.

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Detecting stages of needle penetration into tissues through force estimation at needle tip using fiber Bragg grating sensors

Cited by 17 publications

References 28 publications

Design of a force-measuring setup for colorectal compression anastomosis and first ex-vivo results

Design of a force-measuring setup for colorectal compression anastomosis and first ex-vivo results

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

Spatio-temporal deep learning models for tip force estimation during needle insertion

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