Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches

Flexible needle insertion procedures are common for minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively. Through expansive research in fiber optics, a plethora of bio-compatible, MRI-compatible, optical shape-sensors have been developed to provide real-time shape feedback, such as single-core and multicore fiber Bragg gratings. In this paper, we directly compare single-core fiber-based and multicore fiber-based needle shape-sensing through identically constructed, four-active area sensorized bevel-tip needles inserted into phantom and \exvivo tissue on the same experimental platform. In this work, we found that for shape-sensing in phantom tissue, the two needles performed identically with a $p$-value of $0.164 > 0.05$, but in \exvivo real tissue, the single-core fiber sensorized needle significantly outperformed the multicore fiber configuration with a $p$-value of $0.0005 < 0.05$. This paper also presents the experimental platform and method for directly comparing these optical shape sensors for the needle shape-sensing task, as well as provides direction, insight and required considerations for future work in constructively optimizing sensorized needles

Full 3D motion control for programmable bevel-tip steerable needles

Minimally invasive surgery has been in the focus of many researchers due to its reduced intra- and post-operative risks when compared to an equivalent open surgery approach. In the context of minimally invasive surgery, percutaneous intervention, and particularly, needle insertions, are of great importance in tumour-related therapy and diagnosis. However, needle and tissue deformation occurring during needle insertion often results in misplacement of the needles, which leads to complications, such as unsuccessful treatment and misdiagnosis. To this end, steerable needles have been proposed to compensate for placement errors by allowing curvilinear navigation. A particular type of steerable needle is the programmable bevel-tip steerable needle (PBN), which is a bio-inspired needle that consists of relatively soft and slender segments. Due to its flexibility and bevel-tip segments, it can navigate through 3D curvilinear paths. In PBNs, steering in a desired direction is performed by actuating particular PBN segments. Therefore, the pose of each segment is needed to ensure that the correct segment is actuated. To this end, in this thesis, proprioceptive sensing methods for PBNs were investigated. Two novel methods, an electromagnetic (EM)-based tip pose estimation method and a fibre Bragg grating (FBG)-based full shape sensing method, were presented and evaluated. The error in position was observed to be less than 1.08 mm and 5.76 mm, with the proposed EM-based tip tracking and FBG-based shape reconstruction methods, respectively. Moreover, autonomous path-following controllers for PBNs were also investigated. A closed-loop, 3D path-following controller, which was closed via feedback from FBG-inscribed multi-core fibres embedded within the needle, was presented. The nonlinear guidance law, which is a well-known approach for path-following control of aerial vehicles, and active disturbance rejection control (ADRC), which is known for its robustness within hard-to-model environments, were chosen as the control methods. Both linear and nonlinear ADRC were investigated, and the approaches were validated in both ex vivo brain and phantom tissue, with some of the experiments involving moving targets. The tracking error in position was observed to be less than 6.56 mm.Open Acces

SMART IMAGE-GUIDED NEEDLE INSERTION FOR TISSUE BIOPSY

M.S

Intra-Operative Needle Tracking Using Optical Shape Sensing Technology

RÉSUMÉ Contexte : Les métastases hépatiques colorectales sont la principale cause de décès liée au cancer du foie dans le monde. Au cours de la dernière décennie, il a été démontré que l’ablation par radiofréquence (RFA, pour radiofrequency ablation) est une méthode de traitement percutané très efficace contre ce type de métastases. Cela dit, un positionnement précis de l’embout de l’aiguille utilisé en RFA est essentiel afin de se départir adéquatement de la totalité des cellules cancéreuses. Une technologie prometteuse pour obtenir la forme et la position de l’aiguille en temps réel est basée sur l’utilisation de réseaux de Bragg (FBG, pour fiber Bragg grating) à titre de senseur de contrainte. En effet, ce type de senseurs a une vitesse d’acquisition allant jusqu’à 20 kHz, ce qui est suffisamment rapide pour permettre des applications de guidage en temps réel. Méthode : Les travaux présentés au sein de ce mémoire décrivent le développement d’une technologie, compatible aux systèmes d’imageries par résonance magnétique (IRM), permettant d’effectuer le suivi de la forme de l’aiguille utilisée en RFA. Premièrement, trois fibres contenant une série de réseaux de Bragg ont été collées dans une géométrie spécifique et intégrées à l’intérieur d’une aiguille 20G-150 mm. Ensuite, un algorithme de reconstruction de forme tridimensionnelle a été développé, basé sur les mesures de translation spectrales des FBGs acquises en temps réel durant le guidage de l’aiguille. La position du bout de l’aiguille ainsi que la forme tridimensionnelle complète de celle-ci ont été représentées et comparées à la position de la zone ciblée à la suite d’une simple méthode de calibration. Finalement, nous avons validé notre système de navigation en effectuant une série d’expériences in vitro. La précision du système de reconstruction tridimensionnelle de la forme et de l’orientation de l’aiguille a été évaluée en utilisant deux caméras positionnées perpendiculairement de manière à connaitre la position de l’aiguille dans le système d’axes du laboratoire. L’évaluation de la précision au bout de l’aiguille a quant à elle été faite en utilisant des fantômes précisément conçus à cet effet. Finalement, des interventions guidées en IRM ont été testées et comparées au système de navigation électromagnétique NDI Aurora (EMTS, pour Electromagnétic tracking system) par le biais du FRE (fiducial registration error) et du TRE (target registration error). Résultats: Lors de nos premières expériences in vitro, la précision obtenue quant à la position du bout de l’aiguille était de 0,96 mm pour une déflexion allant jusqu’à ±10,68 mm. À titre comparatif, le système d’Aurora a une précision de 0.84 mm dans des circonstances similaires. Les résultats obtenus lors de nos seconds tests ont démontré que l’erreur entre la position réelle du bout de l’aiguille et la position fournie par notre système de reconstruction de forme est de 1,04 mm, alors qu’elle est de 0,82 mm pour le EMTS d’Aurora. Pour ce qui est de notre dispositif, cette erreur est proportionnelle à l’amplitude de déflexion de l’aiguille, contrairement à l’EMTS pour qui l’erreur demeure relativement constante. La dernière expérience a été effectuée à l’aide d’un fantôme en gélatine, pour laquelle nous avons obtenu un TRE de 1,19 mm pour notre système basé sur les FBG et de 1.06 mm pour le système de navigation par senseurs électromagnétiques (EMTS). Les résultats démontrent que l’évaluation du FRE est similaire pour les deux approches. De plus, l’information fournie par les caméras permet d’estimer la précision de notre dispositif en tout point le long de l’aiguille. Conclusion : En analysant et en interprétant les résultats obtenus lors de nos expériences in vitro, nous pouvons conclure que la précision de notre système de navigation basé sur les FBG est bien adaptée pour l’évaluation de la position du bout et la forme de l’aiguille lors d’interventions RFA des tumeurs du foie. La précision de notre système de navigation est fortement comparable avec celle du système basé sur des senseurs électromagnétiques commercialisé par Aurora. L’erreur obtenue par notre système est attribuable à un mauvais alignement des réseaux de Bragg par rapport au plan associé à la région sensorielle et aussi à la différence entre le diamètre des fibres et celui de la paroi interne de l’aiguille.----------ABSTRACT Background: Colorectal liver metastasis is the leading cause of liver cancer death in the world. In the past decade, radiofrequency ablation (RFA) has proven to be an effective percutaneous treatment modality for the treatment of metastatic hepatic cancer. Accurate needle tip placement is essential for RFA of liver tumors. A promising technology to obtain the real-time information of the shape of the needle is by using fiber Bragg grating (FBG) sensors at high frequencies (up to 20 kHz). Methods: In this thesis work, we developed an MR-compatible needle tracking technology designed for RFA procedures in liver cancer. At first, three fibers each containing a series of FBGs were glued together and integrated inside a 20G-150 mm needle. Then a three-dimensional needle shape reconstruction algorithm was developed, based on the FBG measurements collected in real-time during needle guidance. The tip position and shape of the reconstructed 3D needle model were represented with respect to the target defined in the image space by performing a fiducial-based registration. Finally, we validated our FBG-based needle navigation by doing a series of in-vitro experiments. The shape of the 3D reconstructed needle was compared to measurements obtained from camera images. In addition, the needle tip accuracy was assessed on the ground-truth phantoms. Finally, MRI guided intervention was tested and compared to an NDI Aurora EM tracking system (EMTS) in terms of fiducial registration error (FRE) and target registration error (TRE). Results: In our first in-vitro experiment, the tip tracking accuracy of our FBG tracking system was of 0.96 mm for the maximum tip deflection of up to ±10.68 mm, while the tip tracking accuracy of the Aurora system for the similar test was 0.84 mm. Results obtained from the second in-vitro experiment demonstrated tip tracking accuracy of 1.04 mm and 0.82 mm for our FBG tracking system and Aurora EMTS, respectively for the maximum tip deflection of up to ±16.83 mm. The tip tracking error in the developed FBG-based system reduced linearly with decreasing tip deflection, while the error was similar but randomly varying for the EMTS. The last experiment was done with a gel phantom, yielding a TRE of 1.19 mm and 1.06 mm for the FBG and EM tracking, respectively. Results showed that across all experiments, the computed FRE of both tracking systems was similar. Moreover, actual shape information obtained from the camera images ensured the shape accuracy of our FBG-based needle shape model. Conclusion: By analyzing and interpreting the results obtained from the in-vitro experiments, we conclude that the accuracy of our FBG-based tracking system is suitable for needle tip detection in RFA of liver tumors. The accuracy of our tracking system is nearly comparable to that of the Aurora EMTS. The error given by our tracking system is attributed to the misalignment of the FBG sensors in a single axial plane and also to the gap between the needle's inner wall and the fibers inside

Optical fiber technology enables smart needles for epidurals. An in-vivo swine study

Nowadays, epidural space identification is made by using subjective and manual techniques characterized by failure rates up to 7%. In this work, we propose a fiber optic sensor technology based needle guidance system, that is directly inspired by the most common technique currently used for epidurals; through real-time strain measurements, the fiber Bragg grating integrated inside the needle lumen is able to effectively perceive the typical force drop occurring when the needle enters the epidural space. An in vivo swine study demonstrates the validity of our approach, paving the way for the development of lab-in-a-needle systems

Development of a fiber-based shape sensor for navigating flexible medical tools

Robot-assisted minimally invasive surgical procedure (RAMIS) is a subfield of minimally invasive surgeries with enhanced manual dexterity, manipulability, and intraoperative image guidance. In typical robotic surgeries, it is common to use rigid instruments with functional articulating tips. However, in some operations where no adequate and direct access to target anatomies is available, continuum robots can be more practical, as they provide curvilinear and flexible access. However, their inherent deformable design makes it difficult to accurately estimate their 3D shape during the operation in real-time. Despite extensive model-based research that relies on kinematics and mechanics, accurate shape sensing of continuum robots remains challenging. The state-of-the-art tracking technologies, including optical trackers, EM tracking systems, and intraoperative imaging modalities, are also unsuitable for this task, as they all have shortcomings. Optical fiber shape sensing solutions offer various advantages compared to other tracking modalities and can provide high-resolution shape measurements in real-time. However, commercially available fiber shape sensors are expensive and have limited accuracy. In this thesis, we propose two cost-effective fiber shape sensing solutions based on multiple single-mode fibers with FBG (fiber Bragg grating) arrays and eccentric FBGs. First, we present the fabrication and calibration process of two shape sensing prototypes based on multiple single-mode fibers with semi-rigid and super-elastic substrates. Then, we investigate the sensing mechanism of edge-FBGs, which are eccentric Bragg gratings inscribed off-axis in the fiber's core. Finally, we present a deep learning algorithm to model edge-FBG sensors that can directly predict the sensor's shape from its signal and does not require any calibration or shape reconstruction steps. In general, depending on the target application, each of the presented fiber shape sensing solutions can be used as a suitable tracking device. The developed fiber sensor with the semi-rigid substrate has a working channel in the middle and can accurately measure small deflections with an average tip error of 2.7 mm. The super-elastic sensor is suitable for measuring medium to large deflections, where a centimeter range tip error is still acceptable. The tip error in such super-elastic sensors is higher compared to semi-rigid sensors (9.9-16.2 mm in medium and large deflections, respectively), as there is a trade-off between accuracy and flexibility in substrate-based fiber sensors. Edge-FBG sensor, as the best performing sensing mechanism among the investigated fiber shape sensors, can achieve a tip accuracy of around 2 mm in complex shapes, where the fiber is heavily deflected. The developed edge-FBG shape sensing solution can compete with the state-of-the-art distributed fiber shape sensors that cost 30 times more

Sensorisation of a novel biologically inspired flexible needle

Percutaneous interventions are commonly performed during minimally invasive brain surgery, where a straight rigid instrument is inserted through a small incision to access a deep lesion in the brain. Puncturing a vessel during this procedure can be a life-threatening complication. Embedding a forward-looking sensor in a rigid needle has been proposed to tackle this problem; however, using a rigid needle, the procedure needs to be interrupted if a vessel is detected. Steerable needle technology could be used to avoid obstacles, such as blood vessels, due to its ability to follow curvilinear paths, but research to date was lacking in this respect. This thesis aims to investigate the deployment of forward-looking sensors for vessel detection in a steerable needle. The needle itself is based on a bioinspired programmable bevel-tip needle (PBN), a multi-segment design featuring four hollow working channels. In this thesis, laser Doppler flowmetry (LDF) is initially characterised to ensure that the sensor fulfils the minimum requirements for it to be used in conjunction with the needle. Subsequently, vessel reconstruction algorithms are proposed. To determine the axial and off-axis position of the vessel with respect to the probe, successive measurements of the LDF sensor are used. Ideally, full knowledge of the vessel orientation is required to execute an avoidance strategy. Using two LDF probes and a novel signal processing method described in this thesis, the predicted possible vessel orientations can be reduced to four, a setup which is explored here to demonstrate viable obstacle detection with only partial sensor information. Relative measurements from four LDF sensors are also explored to classify possible vessel orientations in full and without ambiguity, but under the assumption that the vessel is perpendicular to the needle insertion axis. Experimental results on a synthetic grey matter phantom are presented, which confirm these findings. To release the perpendicularity assumption, the thesis concludes with the description of a machine learning technique based on a Long Short-term memory network, which enables a vessel's spatial position, cross-sectional diameter and full pose to be predicted with sub-millimetre accuracy. Simulated and in-vitro examinations of vessel detection with this approach are used to demonstrate effective predictive ability. Collectively, these results demonstrate that the proposed steerable needle sensorisation is viable and could lead to improved safety during robotic assisted needle steering interventions.Open Acces