40 results found
Aktas A, Demircali AA, Secoli R, et al., 2023, Towards a procedure-optimised steerable catheter for deep-seated neurosurgery, Biomedicines, Vol: 11, Pages: 1-17, ISSN: 2227-9059
In recent years, steerable needles have attracted significant interest in relation to minimally invasive surgery (MIS). Specifically, the flexible, programmable bevel-tip needle (PBN) concept was successfully demonstrated in vivo in an evaluation of the feasibility of convection-enhanced delivery (CED) for chemotherapeutics within the ovine model with a 2.5 mm PBN prototype. However, further size reductions are necessary for other diagnostic and therapeutic procedures and drug delivery operations involving deep-seated tissue structures. Since PBNs have a complex cross-section geometry, standard production methods, such as extrusion, fail, as the outer diameter is reduced further. This paper presents our first attempt to demonstrate a new manufacturing method for PBNs that employs thermal drawing technology. Experimental characterisation tests were performed for the 2.5 mm PBN and the new 1.3 mm thermally drawn (TD) PBN prototype described here. The results show that thermal drawing presents a significant advantage in miniaturising complex needle structures. However, the steering behaviour was affected due to the choice of material in this first attempt, a limitation which will be addressed in future work.
Secoli R, Matheson E, Pinzi M, et al., 2022, Modular robotic platform for precision neurosurgery with a bio-inspired needle: system overview and first in-vivo deployment, PLoS One, ISSN: 1932-6203
Segato A, Di Marzo M, Zucchelli S, et al., 2022, Inverse Reinforcement Learning Intra-Operative Path Planning for Steerable Needle, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 69, Pages: 1995-2005, ISSN: 0018-9294
Jamal A, Yuan T, Galvan S, et al., 2022, Insights into infusion-based targeted drug delivery in brain: perspectives, challenges and opportunities, International Journal of Molecular Sciences, Vol: 23, Pages: 3139-3139, ISSN: 1422-0067
Targeted drug delivery in the brain is instrumental in the treatment of lethal brain diseases, such as glioblastoma multiforme, the most aggressive primary central nervous system tumour in adults. Infusion-based drug delivery techniques, which directly administer to the tissue for local treatment, as in convection-enhanced delivery (CED), provide an important opportunity; however, poor understanding of the pressure-driven drug transport mechanisms in the brain has hindered its ultimate success in clinical applications. In this review, we focus on the biomechanical and biochemical aspects of infusion-based targeted drug delivery in the brain and look into the underlying molecular level mechanisms. We discuss recent advances and challenges in the complementary field of medical robotics and its use in targeted drug delivery in the brain. A critical overview of current research in these areas and their clinical implications is provided. This review delivers new ideas and perspectives for further studies of targeted drug delivery in the brain.
Sciortino T, Secoli R, D'Amico E, et al., 2021, Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies, CANCERS, Vol: 13
Trovatelli M, Brizzola S, Zani DD, et al., 2021, Development and in vivo assessment of a novel MRI-compatible headframe system for the ovine animal model, INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, Vol: 17, ISSN: 1478-5951
Segato A, Vece CD, Zucchelli S, et al., 2021, Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 6, Pages: 6061-6067, ISSN: 2377-3766
Pinzi M, Watts T, Secoli R, et al., 2021, Path replanning for orientation-constrained needle steering, IEEE Transactions on Biomedical Engineering, Vol: 68, Pages: 1459-1466, ISSN: 0018-9294
Introduction: Needle-based neurosurgical procedures require high accuracy in catheter positioning to achieve high clinical efficacy. Significant challenges for achieving accurate targeting are (i) tissue deformation (ii) clinical obstacles along the insertion path (iii) catheter control. Objective: We propose a novel path-replanner able to generate an obstacle-free and curvature bounded three-dimensional (3D) path at each time step during insertion, accounting for a constrained target pose and intraoperative anatomical deformation. Additionally, our solution is sufficiently fast to be used in a closed-loop system: needle tip tracking via electromagnetic sensors is used by the path-replanner to automatically guide the programmable bevel-tip needle (PBN) while surgical constraints on sensitive structures avoidance are met. Methods: The generated path is achieved by combining the ”Bubble Bending” method for online path deformation and a 3D extension of a convex optimisation method for path smoothing. Results: Simulation results performed on a realistic dataset show that our replanning method can guide a PBN with bounded curvature to a predefined target pose with an average targeting error of 0.65 ± 0.46 mm in position and 3.25 ± 5.23 degrees in orientation under a deformable simulated environment. The proposed algorithm was also assessed in-vitro on a brain-like gelatin phantom, achieving a target error of 1.81 ± 0.51 mm in position and 5.9 ± 1.42 degrees in orientation. Conclusion: The presented work assessed the performance of a new online steerable needle path-planner able to avoid anatomical obstacles while optimizing surgical criteria. Significance: This method is particularly suited for surgical procedures demanding high accuracy on the desired goal pose under tissue deformations and real-world inaccuracies.
Riva M, Sciortino T, Secoli R, et al., 2021, Glioma <i>biopsies</i> Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples, CANCERS, Vol: 13
Favaro A, Secoli R, Rodriguez y Baena F, et al., 2021, Optimal pose estimation method for a multi-segment, programmable bevel-tip steerable needle, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Publisher: IEEE, Pages: 3232-3238
Pose tracking is fundamental to achieve preciseand safe insertion of a surgical tool for minimally invasiveinterventions. In this work, a method for the estimation of thefull pose of steerable needles is presented. Our approach uses aProgrammable Bevel Tip (PBN) needle with four-segment designas a case study. A novel 3D kinematic model of the PBN isdeveloped and used to predict the full needle pose during theinsertion. The pose prediction is estimated through an ExtendedKalman Filter using the position measurements provided byan electromagnetic sensor located at each tip of the needlesegments. The method estimates also the torsion of the needleshaft that can arise over the insertion of the needle becauseof the shear forces exerted between the needle and the insertionmedium. The feasibility of the proposed solution was validated ina number of experiments in gelatin demonstrating a small errorin position reconstruction (RMSE<0.6mm) and good accuracy incomparison to a bespoke geometric pose reconstruction method.
The supervisory-control method is used in the majority of neurosurgical robots to date where the surgeon makes the high-level decisions, which are then autonomously performed by the robot. In this chapter the differences in the roles of the robots during preoperative and intraoperative procedures are explained. During intraoperative procedures the robot can have either direct interaction or no direct interaction with the human tissues, called active and passive systems, respectively. The flow of information between the robots, the surgical environment, and the surgeons, to enable these forms of interaction, is also discussed. Examples of currently available robotic systems are provided.
Koenig A, Rodriguez y Baena F, Secoli R, 2021, Gesture-Based Teleoperated Grasping for Educational Robotics, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Publisher: IEEE, Pages: 222-228, ISSN: 1944-9445
Favaro A, Secoli R, Rodriguez y Baena F, et al., 2020, Model-Based Robust Pose Estimation for a Multi-Segment, Programmable Bevel-Tip Steerable Needle, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 6780-6787, ISSN: 2377-3766
Matheson E, Secoli R, Galvan S, et al., 2020, Human-robot visual interface for 3D steering of a flexible, bioinspired needle for neurosurgery, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE
Robotic minimally invasive surgery has been a subject of intense research and development over the last three decades, due to the clinical advantages it holds for patients and doctors alike. Particularly for drug delivery mechanisms, higher precision and the ability to follow complex trajectories in three dimensions (3D), has led to interest in flexible, steerable needles such as the programmable bevel-tip needle (PBN). Steering in 3D, however, holds practical challenges for surgeons, as interfaces are traditionally designed for straight line paths. This work presents a pilot study undertaken to evaluate a novel human-machine visual interface for the steering of a robotic PBN, where both qualitative evaluation of the interface and quantitative evaluation of the performance of the subjects in following a 3D path are measured. A series of needle insertions are performed in phantom tissue (gelatin) by the experiment subjects. User could adequately use the system with little training and low workload, and reach the target point at the end of the path with millimeter range accuracy.
Watts TE, Secoli R, Rodriguez y Baena F, 2019, A mechanics-based model for 3D steering of programmable bevel-tip needles, IEEE Transactions on Robotics, Vol: 35, Pages: 371-386, ISSN: 1552-3098
We present a model for the steering of programmable bevel-tip needles, along with a set of experiments demonstrating the 3D steering performance of a new, clinically viable, 4-segment, pre-production prototype. A multi-beam approach, based on Euler-Bernoulli beam theory, is used to model the novel multi-segment design of these needles. Finite element simulations for known loads are used to validate the multi-beam deflection model. A clinically sized (2.5 mm outer diameter), 4-segment programmable bevel-tip needle, manufactured by extrusion of a medical-grade polymer, is used to conduct an extensive set of experimental trials to evaluate the steering model. For the first time, we demonstrate the ability of the 4-segment needle design to steer in any direction with a maximum achievable curvature of 0.0192±0.0014 mm⁻¹. Finite element simulations confirm that the multi-beam approach produces a good model fit for tip deflections, with a root-mean-square deviation (RMSD) in modeled tip deflection of 0.2636 mm. We perform a parameter optimization to produce a best-fit steering model for the experimental trials, with a RMSD in curvature prediction of 1.12×10⁻³ mm⁻¹.
Matheson E, Watts T, Secoli R, et al., 2019, Cyclic motion control for programmable bevel-tip needles 3D steering: a simulation study, ROBIO - IEEE International Conference on Robotics and Biomimetics, Publisher: IEEE
Flexible, steerable, soft needles are desirable inMinimally Invasive Surgery to achieve complex trajectorieswhile maintaining the benefits of percutaneous interventioncompared to open surgery. One such needle is the multi-segmentProgrammable Bevel-tip Needle (PBN), which is inspired by themechanical design of the ovipositor of certain wasps. PBNscan steer in 3D whilst minimizing the force applied to thesurrounding substrate, due to the cyclic motion of the segments.Taking inspiration also from the control strategy of the wasp toperform insertions and lay their eggs, this paper presents thedesign of a cyclic controller that can steer a PBN to produce adesired trajectory in 3D. The performance of the controller isdemonstrated in simulation in comparison to that of a directcontroller without cyclic motion. It is shown that, while thesame steering curvatures can be attained by both controllers,the time taken to achieve the configuration is longer for thecyclic controller, leading to issues of potential under-steeringand longer insertion times.
Watts T, Secoli R, Rodriguez y Baena F, 2018, Needle Steerability Measures: Definition and Application for Optimized Steering of the Programmable Bevel-tip Needle, 2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018)
Watts T, Secoli R, Rodriguez y Baena F, 2018, Modelling the deformation of biologically inspired flexible structures for needle steering, The first International Congress for the Advancement of Mechanism, Machine, Robotics and Mechatronics Sciences 2017, Publisher: Springer, Pages: 67-80, ISSN: 2211-0984
Recent technical advances in minimally invasive surgery have been enabled by the development of new medical instruments and technologies. To date, the vast majority of mechanisms used within a clinical context are rigid, contrasting with the compliant nature of biological tissues. The field of robotics has seen an increased interest in flexible and compliant systems, and in this paper we investigate the behaviour of deformable multi-segment structures, which take their inspiration from the ovipositor design of parasitic wood wasps. These configurable structures have been shown to steer through highly compliant substrates, potentially enabling percutaneous access to the most delicate of tissues, such as the brain. The model presented here sheds light on how the deformation of the unique structure is related to its shape, and allows comparison between different potential designs. A finite element study is used to evaluate the proposed model, which is shown to provide a good fit (root-mean-square deviation 0.2636 mm for 4-segment case). The results show that both 3-segment and 4-segment designs are able to achieve deformation in all directions, however the magnitude of deformation is more consistent in the 4-segment case.
Matheson E, Secoli R, Burrows C, et al., 2018, Cyclic motion control for programmable bevel-tip needles to reduce tissue deformation, Journal of Medical Robotics Research, Vol: 4, ISSN: 2424-905X
Robotic-assisted steered needles aim to accurately control the deflection of the flexible needle’s tip to achieve accurate path following. In doing so, they can decrease trauma to the patient, by avoiding sensitive regions while increasing placement accuracy. This class of needle presents more complicated kinematics compared to straight needles, which can be exploited to produce specific motion profiles via careful controller design and tuning. Motion profiles can be optimized to minimize certain conditions such as maximum tissue deformation and target migration, which was the goal of the formalized cyclic, low-level controller for a Programmable Bevel-tip Needle (PBN) presented in this work. PBNs are composed of a number of interlocked segments that are able to slide with respect to one another. Producing a controlled, desired offset of the tip geometry leads to the corresponding desired curvature of the PBN, and hence desired path trajectory of the system. Here, we propose a cyclical actuation strategy, where the tip configuration is achieved over a number of reciprocal motion cycles, which we hypothesize will reduce tissue deformation during the insertion process. A series of in vitro, planar needle insertion experiments are performed in order to compare the cyclic controller performance with the previously used direct push controller, in terms of targeting accuracy and tissue deformation. It is found that there is no significant difference between the target tracking performance of the controllers, but a significant decrease in axial tissue deformation when using the cyclic controller.
Secoli R, Rodriguez y Baena F, 2018, Experimental validation of curvature tracking with a programmable bevel-tip steerable needle, International Symposium on Medical Robotics, Publisher: IEEE
Needle steering systems are a topic of increasing research interest due to the many potential advantages associated with the ability to reach deep-seated targets while avoiding obstacles. Existing embodiments, such as those designed around a fixed bevel tip, are necessarily disruptive to the substrate, with the potential to cause a target to move away from the insertion trajectory, as well as potentially increasing the extent of tissue trauma at the needle interface, when compared to straight needles. To alleviate these issues, we proposed a biologically inspired design, which can steer without the need for duty-cycle spinning along the insertion axis or any active mechanisms at the tip. In this work, we demonstrate for the first time that our needle is able to steer within a deformable substrate, along with a user-defined trajectory in three-dimensional space. A simplified kinematic model is reported, which is subsequently used to design an adaptive strategy enabling the tracking of arbitrary curvatures along any given reference plane. Experimental results in gelatin are used to validate our model, as well as the performance of the controller under laboratory conditions.
Burrows C, Liu F, Leibinger A, et al., 2017, Multi-target Planar Needle Steering with a Bio-inspired Needle Design, 1st International Conference of IFToMM ITALY (IFIT), Publisher: Springer, Pages: 51-60, ISSN: 2211-0984
Percutaneous intervention is common practice in many diagnostic and therapeutic surgical procedures. Needle steering research aims to extend these by enabling therapies that are not possible with a straight rigid needle. Being able to address multiple targets in one insertion is an example of such a therapy, which would result in reduced overall trauma to the patient and surgery time. However, needle steering remains challenging, as soft tissue is highly compliant and deformable, and thus difficult to interact with. In this work, we develop a new biologically inspired needle design (4 mm outside diameter) and show its capabilities in multiple moving target scenarios. In vitro results in gelatin demonstrate accurate 2D tracking of two virtual targets over 3 target movement rates.
Darwood A, Secoli R, Bowyer SA, et al., 2016, Intraoperative manufacturing of patient specific instrumentation for shoulder arthroplasty: a novel mechatronic approach, Journal of Medical Robotics Research, Vol: 1, ISSN: 2424-905X
Optimal orthopaedic implant placement is a major contributing factor to the long term success of all common joint arthroplasty procedures. Devicessuch as three-dimensional (3D) printed, bespoke guides and orthopaedic robots are extensively described in the literature and have been shownto enhance prosthesis placement accuracy. These technologies, however, have significant drawbacks, such as logistical and temporal inefficiency,high cost, cumbersome nature and difficult theatre integration. A new technology for the rapid intraoperative production of patient specific instrumentation,which overcomes many of the disadvantages of existing technologies, is presented here. The technology comprises a reusable table sidemachine, bespoke software and a disposable element comprising a region of standard geometry and a body of mouldable material. Anatomicaldata from Computed Tomography (CT) scans of 10 human scapulae was collected and, in each case, the optimal glenoid guidewire position wasdigitally planned and recorded. The achieved accuracy compared to the preoperative bespoke plan was measured in all glenoids, from both a conventionalgroup and a guided group. The technology was successfully able to intraoperatively produce sterile, patient specific guides according toa pre-operative plan in 5 minutes, with no additional manufacturing required prior to surgery. Additionally, the average guide wire placement accuracywas 1.58 mm and 6.82◦ degrees in the manual group, and 0.55 mm and 1.76◦ degrees in the guided group, also demonstrating a statisticallysignificant improvement.
Secoli R, Rodriguez y Baena F, 2016, Adaptive path-following control for bio-inspired steerable needles, 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Publisher: IEEE
Needle steering systems have shown potential ad-vantages in minimally invasive surgery in soft-tissue due to theirability to reach deep-seated targets while avoiding obstacles. Ingeneral, the control strategies employed to drive the insertionuse simplified kinematic models, providing limited control ofthe trajectory between an entry site and a deep seated targetin cases of unmodelled tissue-needle dynamics. In this work,we present the first Adaptive Path-Following (APF) controllerfor a bio-inspired multi-part needle, able to steer along three-dimensional (3D) paths within a compliant medium by meansof the cyclical motion of interlocked segments and without theneed for duty-cycle spinning along the insertion axis.The control strategy is outlined in two parts: a high-level con-troller, which provides driving commands to follow a predefined3D path smoothly; and a low-level controller, able to counteractunmodelled tissue-needle nonlinearities and kinematic modeluncertainties. A simulation that mimics the needle’s mechanicalbehavior during insertion is achieved by using an ExperimentalFitting Model (EFM), obtained from previous experimentaltrials. The Simulation results demonstrate the robustness andadaptability of the proposed control strategy.
Liu F, Garriga-Casanovas A, Secoli R, et al., 2016, Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles, IEEE Robotics and Automation Letters, Vol: 1, Pages: 601-608, ISSN: 2377-3766
Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation.
Secoli R, Robinson M, Brugnoli M, et al., 2015, A low-cost, high-field-strength magnetic resonance imaging-compatible actuator, PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE, Vol: 229, Pages: 215-224, ISSN: 0954-4119
Zondervan DK, Secoli R, Darling AM, et al., 2015, Design and Evaluation of the Kinect-Wheelchair Interface Controlled (KWIC) Smart Wheelchair for Pediatric Powered Mobility Training, ASSISTIVE TECHNOLOGY, Vol: 27, Pages: 183-192, ISSN: 1040-0435
Cattilino M, Secoli R, Galvan S, et al., 2014, Development of a Dynamic Soft Tissue Phantom for Cooperative Control Testing in Robotic Surgery, Hamlyn Symposium
Rivera-Rubio J, Alexiou I, Bharath A, et al., 2014, Associating locations from wearable cameras
In this paper, we address a specific use-case of wearable or hand-held camera technology: indoor navigation. We explore the possibility of crowd-sourcing navigational data in the form of video sequences that are captured from wearable or hand-held cameras. Without using geometric inference techniques (such as SLAM), we test video data for navigational content, and algorithms for extracting that content. We do not include tracking in this evaluation; our purpose is to explore the hypothesis that visual content, on its own, contains cues that can be mined to infer a person's location. We test this hypothesis through estimating positional error distributions inferred during one journey with respect to other journeys along the same approximate path. The contributions of this work are threefold. First, we propose alternative methods for video feature extraction that identify candidate matches between query sequences and a database of sequences from journeys made at different times. Secondly, we suggest an evaluation methodology that estimates the error distributions in inferred position with respect to a ground truth. We assess and compare standard approaches from the field of image retrieval, such as SIFT and HOG3D, to establish associations between frames. The final contribution is a publicly available database comprising over 90,000 frames of video-sequences with positional ground-truth. The data was acquired along more than 3 km worth of indoor journeys with a hand-held device (Nexus 4) and a wearable device (Google Glass).
Secoli R, Rodriguez y Baena F, 2014, Rate Dependency during Needle Insertions with a Biologically Inspired Steering System: an Experimental Study, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 856-859, ISSN: 1557-170X
Burrows C, Secoli R, Rodriguez y Baena F, 2013, Experimental characterisation of a biologically inspired 3D steering needle
Percutaneous intervention is a popular minimally invasive surgical technique, as it offers many potential advantages for the patient. Research efforts to date have focussed on improving the accuracy and applicability of this procedure through robotic control, in particular with the application of needle steering systems. Previously, we demonstrated two-dimensional (2D) steering within gelatine, with a prototype of a novel biologically inspired multi-segment needle, the STING. Then, a novel `programmable bevel' concept, where the steering angle of the needle is a function of the offset between segments, was used to control the trajectory taken within the steering plane. This paper presents our first attempt to demonstrate controllable three-dimensional (3D) steering with a new four-segment prototype of the STING. We show that an approximately linear relationship exists between segment offset and curvature of the tip path for a single leading segment, as well as for two segments which are moved forward of the others by an equal amount. This characterisation is then demonstrated with 3D open loop experiments, which show that the established behaviour is applicable for controlled 3D steering along eight principal directions.
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