19 results found
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.
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.
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.
We present a two-part hands-on science outreach demonstration utilizing composite hydrogels to produce realistic models of the human brain. The blends of poly(vinyl alcohol) and Phytagel closely match the mechanical properties of real brain tissue under conditions representative of surgical operations. The composite hydrogel is simple to prepare, biocompatible, and nontoxic, and the required materials are widely available and inexpensive. The first part of the demonstration gives participants the opportunity to feel how soft and deformable our brains are. The second part allows students to perform a mock brain surgery on a simulated tumor. The demonstration tools are suitable for public engagement activities as well as for various student training groups. The activities encompass concepts in polymer chemistry, materials science, and biology.
Khan F, Donder A, Galvan S, et al., 2020, Pose Measurement of Flexible Medical Instruments Using Fiber Bragg Gratings in Multi-Core Fiber, IEEE SENSORS JOURNAL, Vol: 20, Pages: 10955-10962, ISSN: 1530-437X
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.
Pinzi M, Galvan S, Rodriguez y Baena F, 2019, The adaptive hermite fractal tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints, International Journal of Computer Assisted Radiology and Surgery, Vol: 14, Pages: 659-670, ISSN: 1861-6429
PurposeIn the context of minimally invasive neurosurgery, steerable needles such as the one developed within the Horizon2020-funded EDEN2020 project (Frasson et al. in Proc Inst Mech Eng Part H J Eng Med 224(6):775–88, 2010. https://doi.org/10.1243/09544119JEIM663; Secoli and y Baena in IEEE international conference on robotics and automation, 2013) aspire to address the clinical challenge of better treatment for cancer patients. The direct, precise infusion of drugs in the proximity of a tumor has been shown to enhance its effectiveness and diffusion in the surrounding tissue (Vogelbaum and Aghi in Neuro-Oncology 17(suppl 2):ii3–ii8, 2015. https://doi.org/10.1093/neuonc/nou354). However, planning for an appropriate insertion trajectory for needles such as the one proposed by EDEN2020 is challenging due to factors like kinematic constraints, the presence of complex anatomical structures such as brain vessels, and constraints on the required start and target poses.MethodsWe propose a new parallelizable three-dimensional (3D) path planning approach called Adaptive Hermite Fractal Tree (AHFT), which is able to generate 3D obstacle-free trajectories that satisfy curvature constraints given a specified start and target pose. The AHFT combines the Adaptive Fractal Tree algorithm’s efficiency (Liu et al. in IEEE Robot Autom Lett 1(2):601–608, 2016. https://doi.org/10.1109/LRA.2016.2528292) with optimized geometric Hermite (Yong and Cheng in Comput Aided Geom Des 21(3):281–301, 2004. https://doi.org/10.1016/j.cagd.2003.08.003) curves, which are able to handle heading constraints.ResultsSimulated results demonstrate the robustness of the AHFT to perturbations of the target position and target heading. Additionally, a simulated preoperative environment, where the surgeon is able to select a desired entry pose on the patient’s skull, confirms the ability of the method to generate multiple feasible trajectories for a patient-specific case
Favaro A, Cerri L, Galvan S, et al., 2018, Automatic Optimized 3D Path Planner for Steerable Catheters with Heuristic Search and Uncertainty Tolerance, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 9-16, ISSN: 1050-4729
Forte AE, galvan S, Dini D, 2017, Models and tissue mimics for brain shift simulations, Biomechanics and Modeling in Mechanobiology, Vol: 17, Pages: 249-261, ISSN: 1617-7940
Capturing the deformation of human brain during neurosurgical operations is an extremely important task to improve the accuracy or surgical procedure and minimize permanent damage in patients. This study focuses on the development of an accurate numerical model for the prediction of brain shift during surgical procedures and employs a tissue mimic recently developed to capture the complexity of the human tissue. The phantom, made of a composite hydrogel, was designed to reproduce the dynamic mechanical behaviour of the brain tissue in a range of strain rates suitable for surgical procedures. The use of a well-controlled, accessible and MRI compatible alternative to real brain tissue allows us to rule out spurious effects due to patient geometry and tissue properties variability, CSF amount uncertainties, and head orientation. The performance of different constitutive descriptions is evaluated using a brain–skull mimic, which enables 3D deformation measurements by means of MRI scans. Our combined experimental and numerical investigation demonstrates the importance of using accurate constitutive laws when approaching the modelling of this complex organic tissue and supports the proposal of a hybrid poro-hyper-viscoelastic material formulation for the simulation of brain shift.
Tan Z, Bernardini A, Konstantinou I, et al., 2017, Diffusion Measurement and Modelling, European Robotics Forum 2017
Synthetic phantoms are valuable tools for training, research and development in traditional and computer aided surgery, but complex organs, such as the brain, are difficult to replicate. Here, we present the development of a new composite hydrogel capable of mimicking the mechanical response of brain tissue under loading. Our results demonstrate how the combination of two different hydrogels, whose synergistic interaction results in a highly tunable blend, produces a hybrid material that closely matches the strongly dynamic and non-linear response of brain tissue. The new synthetic material is inexpensive, simple to prepare, and its constitutive components are both widely available and biocompatible. Our investigation of the properties of this engineered tissue, using both small scale testing and life-sized brain phantoms, shows that it is suitable for reproducing the brain shift phenomenon and brain tissue response to indentation and palpation.
Tan Z, Forte AE, Galvan S, et al., 2016, Composite Hydrogel: a New Tool for Reproducing the Mechanical Behaviour of Soft Human Tissues, Biotribology 2016
Forte AE, Galvan S, Dini D, 2015, Biomechanics of the brain: experimental investigation, numerical simulation, design of advanced phantoms for robotic surgery, ITC 2015
Forte AE, galvan S, manieri F, et al., 2014, A Novel Composite Phantom for Brain Tissue, EMBC 2014
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
Rasin I, Pekar Z, Sadowsky O, et al., 2014, Real-Time Modelling of Intra-operative Brain Shift Based on Video Tracking, The Hamlyn Symposium on Medical Robotics 2014
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