Insights into Infusion-Based Targeted Drug Delivery in the Brain: Perspectives, Challenges and Opportunities.

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.

Inverse Reinforcement Learning Intra-Operative Path Planning for Steerable Needle.

OBJECTIVE: This paper presentsa safe and effective keyhole neurosurgery intra-operative planning framework for flexible neurosurgical robots. The framework is intended to support neurosurgeons during the intra-operative procedure to react to a dynamic environment. METHODS: The proposed system integrates inverse reinforcement learning path planning algorithm combined with 1) a pre-operative path planning framework for fast and intuitive user interaction, 2) a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion and 3) a simulated robotic system. RESULTS: Simulation results performed on a human brain dataset show that the inverse reinforcement learning intra-operative planning method can guide a steerable needle with bounded curvature to a predefined target pose with an average targeting error of 1.34 ± 0.52 (25 th = 1.02, 75 th = 1.36) mm in position and 3.16 ± 1.06 (25 th = 2, 75 th = 4.94) degrees in orientation under a deformable simulated environment, with a re-planning time of 0.02 sec and a success rate of 100%. CONCLUSION: With this work, we demonstrate that the presented intra-operative steerable needle path planner is able to avoid anatomical obstacles while optimising surgical criteria. SIGNIFICANCE: The results demonstrate that the proposed method is fast and can securely steer flexible needles with high accuracy and robustness.

Modular robotic platform for precision neurosurgery with a bio-inspired needle: System overview and first in-vivo deployment.

Over the past 10 years, minimally invasive surgery (MIS) has shown significant benefits compared to conventional surgical techniques, with reduced trauma, shorter hospital stays, and shorter patient recovery times. In neurosurgical MIS procedures, inserting a straight tool (e.g. catheter) is common practice in applications ranging from biopsy and laser ablation, to drug delivery and fluid evacuation. How to handle tissue deformation, target migration and access to deep-seated anatomical structures remain an open challenge, affecting both the preoperative planning phase and eventual surgical intervention. Here, we present the first neurosurgical platform in the literature, able to deliver an implantable steerable needle for a range of diagnostic and therapeutic applications, with a short-term focus on localised drug delivery. This work presents the system's architecture and first in vivo deployment with an optimised surgical workflow designed for pre-clinical trials with the ovine model, which demonstrate appropriate function and safe implantation.

Path Replanning for Orientation-Constrained Needle Steering.

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 Adaptive Hermite Fractal Tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints.

PURPOSE: In 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. METHODS: We 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. RESULTS: Simulated 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. CONCLUSIONS: The AHFT method can be adopted in any field of application where a 3D path planner with kinematic and heading constraints on both start and end poses is required.

Models and tissue mimics for brain shift simulations.

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.