145 research outputs found

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

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    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

    Optical Multicore Fiber Shape Sensors. A numerical and experimental performance assessment

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    [EN] Structural Health Monitoring (SHM) is a discipline that quantitatively assesses the integrity and performance of infrastructures, relying on sensors, and support the development of efficient Maintenance and Rehabilitation (M&R) plans. Optical Multicore Fiber (MCF) Shape Sensors offer an innovative alternative to traditional methods and enable the reconstruction of the deformed shape of structures directly and in real-time, with no need of computation models or visual contact and exploiting all the advantages of Optical Fiber Sensors (OFS) technology. Despite the intense research efforts centered on this topic by research groups worldwide, a comprehensive investigation on the parameters that influence the performance of these sensors has not been conducted yet. The first part of the thesis presents a numerical study that examines the effects of strain measurement accuracy and core position errors on the performance of optical multicore fiber shape sensors in sensing three-dimensional curvature, which is at the basis of shape reconstruction. The analysis reproduces the strain measurement process using Monte Carlo Method (MCM) and identifies several parameters which play a key role in the phenomenon, including core spacing (distance between outer cores and sensor axis), number of cores and curvature measured. Finally, a set of predictive models were calibrated, by fitting the results of the simulations, to predict the sensors performance. Afterward, an experimental study is proposed to evaluate the performance of optical multicore fiber in sensing shape, with particular focus on the influence of strain sensors length. Two shape sensors were fabricated, by inscribing long (8.0 mm) and short (1.5 mm) Fiber Bragg Gratings (FBG) into the cores of a multicore seven-core fiber. Thus, the performance of the two sensors was assessed and compared, at all the necessary phases for shape reconstruction: strain sensing, curvature calculation and shape reconstruction. To conclude, an innovative approach, based on the Saint-Venant's Torsion Theory, is presented to determine the twisting of multicore fiber and to compensate the errors due to twisting during shape reconstruction. The efficiency of the theoretical approach was then corroborated performing a series of twisting tests on a shape sensor, fabricated by inscribing FBGs sensors into an optical spun multicore seven-core fiber. The investigation of the mechanical behavior of multicore optical shape sensors has synergically involved diverse disciplines: Solid Mechanics, Photonics, Statistics and Data Analysis. Such multidisciplinary research has arisen from the prolific cooperation between the Institutes of the Institute of Science and Technology of Concrete (ICITECH) and the Institute of Telecommunications and Multimedia Applications (iTEAM) - Photonics Research Labs (PRL) - of Universitat Politècnica de València (UPV), in addition to valuable collaboration with other members of the European ITN-FINESSE project, to which this work belongs. This research work aims to enhance the performance optical multicore fiber shape sensors and support the development of new sensor geometries, with great potential for structural health monitoring applications.[ES] La Monitorización de la Salud Estructural (MSE) evalúa cuantitativamente la integridad y el comportamiento de las infraestructuras y permite desarrollar planes eficaces de Mantenimiento y Rehabilitación (M&R), utilizando los datos de los sensores. Sensores de forma basados en fibra óptica multinúcleo ofrecen una alternativa a los métodos tradicionales y permiten la reconstrucción de la deformada de estructuras de forma directa y en tiempo real, sin necesidad de modelos de cálculo o contacto visual y con todas las ventajas de la tecnología de los Sensores de Fibra Óptica (SFO). A pesar de los grandes esfuerzos en la investigación centrada en este tema por parte de los grupos de investigación de todo el mundo, todavía no se ha realizado una investigación exhaustiva que estudie los parámetros que influyen en el comportamiento de estos sensores. En la primera parte de la tesis se presenta un estudio numérico en el que se examinan los efectos de la precisión de la medición de la tensión y los errores de posición del núcleo en el comportamiento de los sensores de forma basados en fibra óptica multinúcleo para definir la curvatura tridimensional, que es la base de la reconstrucción de la forma. El análisis reproduce el proceso de medición de la tensión utilizando el método de Monte Carlo (MC) e identifica una serie de parámetros que desempeñan un papel en el proceso, entre ellos la separación del núcleo (distancia entre los núcleos exteriores y el eje del sensor), el número de núcleos y la curvatura medida. Por último, se calibró un conjunto de modelos de predicción ajustando los resultados de las simulaciones para predecir el comportamiento de los sensores. A continuación, se propone un estudio experimental para evaluar el comportamiento de los sensores de forma basado en fibra óptica multinúcleo, con especial atención en la influencia de la longitud de los sensores de deformación. Se fabricaron dos sensores de forma, inscribiendo Fiber Bragg Gratings (FBG) con longitudes de 8,0 mm y 1,5 mm en los núcleos de una fibra multinúcleo de siete núcleos. Así, se evaluó y comparó el comportamiento de los dos sensores en todas las fases necesarias para la reconstrucción de la forma, incluyendo la medición de la tensión, el cálculo de la curvatura y la reconstrucción de la forma. Para concluir, se presenta un enfoque innovador, basado en la Teoría de la Torsión de Saint-Venant, para determinar la torsión de la fibra multinúcleo y compensar los errores debidos a la torsión durante la reconstrucción de la forma. La eficiencia del enfoque teórico fue verificada realizando una serie de pruebas de torsión en un sensor de forma, fabricado inscribiendo los sensores de FBGs en una fibra óptica multinúcleo torcida y siete núcleos. La investigación del comportamiento mecánico de los sensores ópticos de forma multinúcleo ha involucrado sinérgicamente diversas disciplinas: Mecánica del sólido, Fotónica, Estadística y Análisis de datos. Esta investigación multidisciplinaria ha surgido de la prolífica cooperación entre el Instituto de Ciencia y Tecnología del Hormigón (ICITECH) y el Instituto de Telecomunicaciones y Aplicaciones Multimedia (iTEAM) - Laboratorio de Investigación Fotónica (LIF) - de la Universidad Politécnica de Valencia (UPV), además de la valiosa colaboración con otros miembros del proyecto europeo ITN-FINESSE, al que pertenece este trabajo. Este trabajo de investigación puede permitir mejorar el comportamiento de los sensores de forma basados en fibra óptica multinúcleo y apoyar el desarrollo de nuevas geometrías de sensores, con un gran potencial para aplicaciones de control de la salud estructural.[CA] Structural Health Monitoring (SHM) avalua quantitativament la integritat i el comportament de les infraestructures i permet desenrotllar plans eficaços de Maintenance and Rehabilitation (M&R), utilitzant les dades dels sensors. Optical Multicore Fiber (MCF) Shape Sensors oferixen una alternativa als mètodes tradicionals i permeten la reconstrucció de la forma de la deformació de les estructures de forma directa i en temps real, sense necessitat de models de càlcul o contacte visual i amb tots els avantatges de l'Optical Fiber Sensors (OFS) Technology. A pesar dels grans esforços en la investigació centrada en aquest tema per part dels grups d'investigació de tot el món, encara no s'ha realitzat una investigació exhaustiva que estudie els paràmetres que influïxen en el comportament d'aquestos sensors. En la primera part de la tesi es presenta un estudi numèric en què s'examinen els efectes de la precisió del mesurament de la tensió i els errors de posició del nucli en el comportament dels sensors de forma basats en fibra òptica multinucli per a definir la curvatura tridimensional, que és la base de la reconstrucció de la forma. L'anàlisi reproduïx el procés de mesurament de la tensió utilitzant el mètode de Monte Carlo (MC) i identifica una sèrie de paràmetres que exercixen un paper en el procés, entre ells la separació del nucli (distància entre els nuclis exteriors i l'eix del sensor), el nombre de nuclis i la mesura de la curvatura. Finalment, es va calibrar un conjunt de models de predicció ajustant els resultats de les simulacions per a predir el comportament dels sensors. A continuació, es proposa un estudi experimental per a avaluar el comportament dels sensors de forma basat en fibra òptica multinucli, amb especial atenció en la influència de la longitud dels sensors de deformació. Es van fabricar dos sensors de forma, inscrivint Fiber Bragg Gratings (FBG) amb longituds de 8,0 mm i 1,5 mm en els nuclis d'una fibra multinucli de set nuclis. Així, es va avaluar i es va comparar el comportament dels dos sensors en totes les fases necessàries per a la reconstrucció de la forma, incloent el mesurament de la tensió, el càlcul de la curvatura i la reconstrucció de la forma. Per a concloure, es presenta un enfocament innovador, basat en la Teoria de la Torsió de Saint-Venant, per a determinar la torsió de la fibra multinucli i compensar els errors deguts a la torsió durant la reconstrucció de la forma. L'eficiència de l'enfocament teòric va ser verificada realitzant una sèrie de proves de torsió en un sensor de forma, fabricat inscrivint els sensors de FBGs en una fibra òptica de set nuclis de filat múltiple. La investigació del comportament mecànic dels sensors òptics de forma multinucli ha involucrat sinèrgicament diverses disciplines: Mecànica del sòlid, Fotónica, Estadística i Anàlisi de dades. Aquesta investigació multidisciplinària ha sorgit de la prolífica cooperació entre l'Institut de Ciència i Tecnologia del Formigó (ICITECH) i l'Institut de Telecomunicacions i Aplicacions Multimèdia (iTEAM) - Laboratori de investigación fotònica (LIF) - de la Universitat Politècnica de València (UPV), a més de la valuosa col·laboració amb altres membres del projecte europeu ITN- FINESSE, al qual pertany aquest treball. Aquest treball d'investigació pot permetre millorar el comportament dels sensors de forma basats en fibra òptica multinucli i ajudar al desenrotllament de noves geometries de sensors, amb un gran potencial per a aplicacions de control de la salut estructural.Floris, I. (2020). Optical Multicore Fiber Shape Sensors. A numerical and experimental performance assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/148715TESI

    Recent developments in fibre optic shape sensing

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    This paper presents a comprehensive critical review of technologies used in the development of fibre optic shape sensors (FOSSs). Their operation is based on multi-dimensional bend measurements using a series of fibre optic sensors. Optical fibre sensors have experienced tremendous growth from simple bend sensors in 1980s to full three-dimensional FOSSs using multicore fibres in recent years. Following a short review of conventional contact-based shape sensor technologies, the evolution trend and sensing principles of FOSSs are presented. This paper identifies the major optical fibre technologies used for shape sensing and provides an account of the challenges and emerging applications of FOSSs in various industries such as medical robotics, industrial robotics, aerospace and mining industry

    Reconstruction 3D de la forme d'aiguilles chirurgicales en utilisant la réflectométrie fréquentielle dans des fibres optiques

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    L’objectif principal de ce projet de recherche est d’effectuer de la reconstruction de forme d’aiguilles chirurgicales en insérant des fibres optiques à l’intérieur. En mesurant les contraintes le long des fibres optiques, on peut facilement obtenir la courbure des fibres. Trois fibres sont donc utilisées, collées dans une géométrie triangulaire de manière à ce que la différence entre leur courbure fournisse l’information nécessaire, avec une résolution plus élevée, pour orienter cette courbure dans un espace tridimensionnel. Puisque la méthode utilisée se base uniquement sur l’utilisation de fibres optiques, on peut extrapoler les possibles applications à des cathéters, des côlonoscopes, ou n’importe quels instruments chirurgicaux minimalement invasifs dont la position dans le corps est importante à connaitre pour maximiser les chances de succès de l’intervention chirurgicale ou éviter des perforations à l’intérieur du corps. Jusqu’à présent, l’approche la plus répandue pour ce genre d’applications est l’utilisation de réseaux de Bragg (« fibre Bragg grating » : FBG) pour mesurer la tension dans la fibre. La meilleure précision recensée dans la littérature avec cette approche est d’environ 0.28mm, qui correspond à l’erreur moyenne de la position du bout de l’aiguille. Pour obtenir cette précision, deux senseurs sont utilisés et chaque senseur comporte trois réseaux de Bragg, soit un dans chacune des trois fibres utilisées (donc un total de six FBGs). Plusieurs études ont été effectuées sur des dispositifs semblables, comportant plus ou moins de FBGs séparés de distances différentes. La plupart de ces études recensent des précisions sur la reconstruction de forme de l’ordre de quelques millimètres. Cela étant dit, cette approche pour mesurer la tension dans les fibres est discrète ; l’information sur la tension est donc obtenue uniquement aux endroits où les réseaux de Bragg sont inscrits et des approximations sont nécessaires pour reconstruire la forme complète de l’aiguille. Ce projet de recherche suggère donc l’utilisation d’une approche sensorielle peu étudiée jusqu’à présent pour ce type d’applications. Cette approche, contrairement aux FBGs, est pleinement distribuée. Notre hypothèse de départ est donc qu’en effectuant des mesures de contraintes de manières distribuées, une meilleure précision peut être obtenue sur la reconstruction de la forme d’instruments chirurgicaux minimalement invasifs puisqu’elle n’implique plus l’utilisation d’approximations.----------Abstract The main objective of the research project is to track the shape of minimally invasive surgical tools (mainly needles) by inserting optical fibers into them. By measuring the strain along the fibers, we can easily relate it to the curvature of the fibers. Using three fibers glued together in a triangular geometry, the difference in the measured curvature of each fiber allows one to orientate the curvature in a 3D frame. Since the approach for shape tracking is strictly based on the insertion of optical fibers inside the restricted space available in minimally invasive surgical tools, it can be used with many types of surgical tools such as catheter needles, colonoscopes, or any other remotely controlled instrument. The knowledge of the position of the device inside the human body is of paramount importance to maximise the success of the intervention. Up to now, the most studied approach for shape tracking using optical fibers is based on fiber Bragg gratings (FBGs), which are useful devices to measure the strain in fibers. To the best of our knowledge, the best precision reached in the literature based on FBGs is ~0.28mm, corresponding to the accuracy in the predicted needle tip position. To reach this precision, two sensors were used, each one containing a set of three fibres with 3 FBGs (one in each fiber) for a total of 6 FBGs. More studies have been made using similar devices, with more or less number of FBG sensors separated by different distances. Most of these studies achieve an accuracy in the order of few millimeters. However, this approach to measure strain along the fibers is completely discrete since the strain is only known at the positions where the FBGs are located. Approximations are thus necessary to extrapolate the strains to recover the whole shape of the needle. This project suggests a truly distributed approach, different to the discrete FBGs technique, which has received little attention up to now for this type of applications. Our first hypothesis is that the precision of the shape tracking can be enhanced by using truly distributed strain sensing (instead of discrete sensing) since approximations are not needed to obtain the shape of the entire needle. This approach is based on optical frequency domain reflectometry (OFDR), which is an interferometric method frequently used to measure the attenuation along fibers. Indeed, OFDR is based on Rayleigh scattering, which is caused by a random distribution of refractive index on a microscopic scale in the fiber core

    A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

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    Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions

    Looking Forward with Minimally Invasive Ultrasound

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    Minimally invasive procedures are increasingly replacing traditional open surgeries due to their shorter recovery time, reduced patient pain, reduced risk of infection and less trauma. However, since the physician has no direct view of the working field, visualization of these complex interventions is critical for success. Forward-looking (FL) ultrasound image guidance can aid minimally invasive procedures providing visual feedback of the working field, instrument location and treatment progress. Currently there are no clinically available devices that can provide minimally invasive 3D FL imaging. In this thesis we explored several innovative solutions towards miniaturized 3D FL imaging. We looked into methods to solve both hardware and image-related challenges resulting in mainly two approaches. The first approach consists in the realization of a complex multi-element transducer with an optimized design and an efficient interconnection and integration scheme. The second approach consists in the use of s

    ПОРІВНЯЛЬНА ЕФЕКТИВНІСТЬ КЛАСИФІКАТОРІВ ЗОБРАЖЕНЬ ПІД ЧАС РОЗПІЗНАВАННЯ ЗОН ІНТЕРЕСУ ПРИ ЛАПАРОСКОПІЧНИХ ВТРУЧАННЯХ

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    Background. The purpose of the study is to evaluate the effectiveness of the automatic computer diagnostic (ACD) systems developed on the basis of two classifiers — HAAR features cascade and AdaBoost for the detection of appendicitis and metastatic damages of the liver. Materials and methods. For the classifiers training the images/frames, which have been cropped out from video gained in the course of laparoscopic diagnostics were used. Namely, RGB frames, and gamma-corrected RGB frames and converted into HSV have been explored. Also descriptors were extracted from images with the modified method of Local Binary Pattern (LBT), which includes data on color characteristics («modified color LBT» — MCLBT) and textural ones were used later on for AdaBoost classifier training. After cessation of training the tests were performed with the aim of the estimation of effectiveness of recognition. Test session images were different from those ones which have been used for training of the classifier. Results. The highest recall for appendicitis diagnostics was achieved after training of AdaBoost with MCLBT descriptors extracted from RGB images—0,745, and in case for metastatic damages diagnostics — 0,902. Hence developed AdaBoost based CAD system achieved 74,4 % correct classification rate (accuracy) for appendicitisc and 89,3 % for metastatic images. The accuracy of HAAR features classifier was highest in case of metastatic foci identification and achieved 0,672 (RGB) — 0,723 (HSV) values. Conclusions. Haar features based cascade classifier turned to be less effective when compared with AdaBoost classifier trained with MCLBT descriptors.В работе представлено сравнительные оценки эффективности систем автоматизированной компьютерной диагностики (АКД), разработанных на основе двух классификаторов — каскада дескрипторов Хаара и AdaBoost, во время лапароскопической диагностики аппендицита и метастазов печени. Для обучения применяли изображения, а также гамма-коррегированные и конвертированные в HSV шкалу RGB изображения, полученные при лапароскопической диагностике. Дескрипторы, которые использовали для обучения классификатора AdaBoost получали при помощи метода локального бинарного паттерна (ЛБП), который включал информационные показатели цвета («модифицированный цвет ЛБП» — MЦЛБП), а также показатели текстуры. После завершения обучения проводили тест оценки эффективности диагностики, при котором использовали изображения, неприменяемые для обучения. Наиболее высоким показатель полноты (recall) был при тестовой диагностике аппендицита с помощью классификатора AdaBoost в обучении применяли дескрипторы МЦЛБП, полученные при анализе RGB изображений — 0,745, а при диагностике метастазов печени — 0,902. Также корректность диагностики (accuracy) составила 74,4 % при диагностике аппендицита и 89,3 % при диагностике метастазов печени. Корректность диагностики при применении классификатора Хаара была наиболее высокой при диагностике метастазов печени и составила 0,672 при использовании RGB изображений и 0,723 — при обучении HSV изображениями. Диагностика с применением классификатора Хаара менее эффективна по сравнению с диагностикой, осуществляемой с применением классификатора AdaBoost, обучение последнего проводили с применением дескрипторов МЦЛБП.У роботі представлено порівняльне оцінювання ефективності систем автоматизованої комп'ютерної діагностики, розроблених на основі двох класифікаторів — каскаду дескрипторів Хаара та AdaBoost, під час лапароскопічної діагностики апендициту та метастазів печінки. Для навчання використовували зображення, а також гама-кореговані та конвертовані у HSV шкалу кольори RGB зображення, отримані під час лапароскопічної діагностики. Дескриптори, що використовували для навчання класифікатора AdaBoost отримували за допомогою методу локального бінарного патерну, який включав інформаційні показники кольору, а також показники текстури. Після завершення навчання проводили тест оцінювання ефективності діагностики при якому використовували зображення, що не застосовували для навчання. Найбільш високим показник повноти (recall) був при тестовій діагностиці апендициту за допомогою навчання класифікатора AdaBoost дескрипторами модифікованого кольору локального бінарного патерну, отриманими з RGB зображень, — 0,745, а під час діагностики метастазів печінки — 0,902. Також коректність діагностики (accuracy) склала 74,4 % під час діагностики апендициту та 89,3 % при діагностиці метастазів печінки. Коректність діагностики із застосуванням класифікатора Хаара була найбільш високою за умови діагностики метастазів печінки та склала 0,672 при використанні RGB зображень, 0,723 — при навчанні HSV зображеннями. Діагностика із застосуванням класифікатора Хаара є менш ефективною порівняно з діагностикою, що здійснювалась із застосуванням класифікатора AdaBoost, навчання якого здійснювали із застосуванням дескрипторів модифікованого кольору локального бінарного патерну
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