Karim Belharet

Endovascular navigation of a ferromagnetic microrobot using MRI-based predictive control

This paper presents real-time MRI-based control of a ferromagnetic microcapsule for endovascular navigation. The concept was studied for future development of microdevices designed to perform minimally invasive interventions in remote sites accessible through the human cardiovascular system. A system software architecture is presented illustrating the different software modules to allow navigation of a microdevice in blood vessels, namely: (i) vessel path planner, (ii) magnetic gradient steering, (iii) tracking and (iv) closed-loop navigation control. First, the position recognition of the microrobot into the blood vessel is extracted using Frangi vesselness filtering from the pre-operation images. Then, a set of minimal trajectory is predefined, using FMM, to guide the microrobot from the injection point to the tumor area through the anarchic vessel network. Based on the pre-computed path, a GPC is proposed for robust time-multiplexed navigation along a 2D path in presence of pulsative flow. The simulation results suggest the validation of the proposed image processing and control algorithms. A series of disturbances introduced in the presence and absence of closed-loop control affirms the robustness and effectiveness of this predictive control system.

Three-Dimensional Controlled Motion of a Microrobot using Magnetic Gradients

This paper presents the endovascular navigation of a ferromagnetic microdevice using magnetic resonance imaging (MRI)-based predictive control. The concept was studied for the future development of microrobots designed to perform minimally invasive interventions in remote sites accessible through the human cardiovascular system. A system software architecture is presented illustrating the different software modules to allow three-dimensional (3-D) navigation of a microdevice in blood vessels, namely: (i) vessel path extraction, (ii) magnetic gradient steering, (iii) tracking and (iv) closed-loop navigation control. First, the navigation path of the microrobot into the blood vessel is extracted using the Fast Marching Method from the pre-operation images (3-D MRI imaging) to guide the microrobot from the injection point to the tumor area through the anarchic vessel network. Based on the pre-computed path, a Model Predictive Controller is proposed for robust time-multiplexed navigation along a 3-D path in the presence of pulsative flow. The simulation results suggest the validation of the proposed image processing and control algorithms.

Simulation and Planning of a Magnetically Actuated Microrobot Navigating in the Arteries

This paper presents a preoperative microrobotic surgical simulation and planning application. The main contribution is to support computer-aided minimally invasive surgery (MIS) procedure using untethered microrobots that have to navigate within the arterial networks. We first propose a fast interactive application (with endovascular tissues) able to simulate the blood flow and microrobot interaction. Second, we also propose a microrobotic surgical planning framework, based on the anisotropic fast marching method (FMM), that provides a feasible pathway robust to biomedical navigation constraints. We demonstrate the framework performance in a case study of the treatment of peripheral arterial diseases.

MRI-based Microrobotic system for the Propulsion and Navigation of Ferromagnetic Microcapsules

Abstract This paper presents real-time MRI-based control of a ferromagnetic microcapsule for endovascular navigation. The concept was studied for future development of microdevices designed to perform minimally invasive interventions in remote sites accessible through the human cardiovascular system. A system software architecture is presented illustrating the different software modules to allow 3-D navigation of a microdevice in blood vessels, namely: (i) vessel path planner, (ii) magnetic gradient steering, (iii) tracking and (iv) closed-loop navigation control. First, the position recognition of the microrobot into the blood vessel is extracted using Frangi vesselness filtering from the pre-operation images (3-D MRI imaging). Then, a set of minimal trajectories is predefined, using path-planning algorithms, to guide the microrobot from the injection point to the tumor area through the anarchic vessel network. Based on the pre-computed path, a Generalized Predictive Controller (GPC) is proposed for robust time-multiplexed navigation along a two-dimensional (2D) path in presence of pulsative flow.

Control of a magnetic microrobot navigating in microfluidic arterial bifurcations through pulsatile and viscous flow

Navigating in bodily fluids to perform targeted diagnosis and therapy has recently raised the problem of robust control of magnetic microrobots under real endovascular conditions. Various control approaches have been proposed in the literature but few of them have been experimentally validated. In this paper, we point out the problem of navigation controllability of magnetic microrobots in high viscous fluids and under pulsatile flow for endovascular applications. We consider the experimental navigation along a desired trajectory, in a simplified millimeter-sized arterial bifurcation, operating in fluids at the low-Reynolds-number regime where viscous drag significantly dominates over inertia. Different viscosity environments are tested under a systolic pulsatile flow compatible with heart beating. The control performances in terms tracking, robustness and stability are then experimentally demonstrated.

Optimal structure of particles-based superparamagnetic microrobots: application to MRI guided targeted drug therapy

This paper presents an optimal design strategy for therapeutic magnetic micro carriers (TMMC) guided in real time by a magnetic resonance imaging (MRI) system. As aggregates of TMMCs must be formed to carry the most amount of drug and magnetic actuation capability, different clustering agglomerations could be arranged. Nevertheless, its difficult to predict the hydrodynamic behavior of any arbitrary-shaped object due to the nonlinear hydrodynamic effects. Indeed, the drag effect is related not only to the properties of the bolus but also to its interaction with the fluid viscosity, the free-stream velocity and the container geometry. In this work, we propose a mathematical framework to optimize the TMMC aggregates to improve the steering efficiency in experimental endovascular conditions. The proposed analysis is carried out on various sizes and geometries of microcarrier: spherical, ellipsoid-like, and chain-like of microsphere structures. We analyze the magnetophoretic behavior of such designs to exhibit the optimal configuration. Based on the optimal design of the boluses, experimental investigations were carried out in mm-sized fluidic artery phantoms to demonstrate the steerability of the magnetic bolus using a proof-of-concept setup. The experiments demonstrate the steerability of the magnetic bolus under different velocity, shear-stress, and trajectory constraints with a laminar viscous fluidic environment. Preliminary experiments with a MRI system confirm the feasibility of the steering of these TMMCs in hepatic artery microchannel phantom.

3D MRI-based predictive control of a ferromagnetic microrobot navigating in blood vessels

This paper presents an endovascular navigation of a ferromagnetic microdevice using a MRI-based predictive control. The concept was studied for future development of microrobot designed to perform minimally invasive interventions in remote sites accessible through the human cardiovascular system. A system software architecture is presented illustrating the different software modules to allow 3D navigation of a microdevice in blood vessels, namely: (i) vessel path extraction, (ii) magnetic gradient steering, (iii) tracking and (iv) closed-loop navigation control. First, the navigation path of the microrobot into the blood vessel is extracted using Fast Marching Method (FMM) from the pre-operation images (3D MRI imaging) to guide the microrobot from the injection point to the tumor area through the anarchic vessel network. Based on the pre-computed path, a Model Predictive Controller (MPC) is proposed for robust navigation along a 3D path. The simulation results suggest the validation of the proposed image processing and control algorithms.

Optimal control of multiple magnetic microbeads navigating in microfluidic channels

This paper presents an optimal control strategy for navigation of multiple magnetic microbeads for future drug targeting applications. To transport the drugs, we use therapeutic magnetic microbeads as navigable agents controlled by magnetic gradients. The main difficulty is to control independently each therapeutic agent along a trajectory with the same magnetic gradient fields. This study proposes an optimal control methodology to control a group of different therapeutic agents at desired states. Based on a dynamic model of group of magnetic microbeads, controllability and observability conditions are formulated and simulated. Based on the proposed theoretical analysis a linear quadratic with integral action control (LQI) has been chosen to be applied to the microbeads system. Finally, an experimental investigation is carried out in millimeter-sized fluidic artery vessels to demonstrate the controllability and stability of two magnetic microbeads under different velocity and trajectory constraints with a laminar viscous fluidic environment.

MRI-based dynamic tracking of an untethered ferromagnetic microcapsule navigating in liquid

ABSTRACT The propulsion of ferromagnetic objects by means of MRI gradients is a promising approach to enable new forms of therapy. In this work, necessary techniques are presented to make this approach work. This includes path planning algorithms working on MRI data, ferromagnetic artifact imaging and a tracking algorithm which delivers position feedback for the ferromagnetic objects, and a propulsion sequence to enable interleaved magnetic propulsion and imaging. Using a dedicated software environment, integrating path-planning methods and real-time tracking, a clinical MRI system is adapted to provide this new functionality for controlled interventional targeted therapeutic applications. Through MRI–based sensing analysis, this article aims to propose a framework to plan a robust pathway to enhance the navigation ability to reach deep locations in the human body. The proposed approaches are validated with different experiments.

Modeling of Optimal Targeted Therapies Using Drug-Loaded Magnetic Nanoparticles for Liver Cancer

To enhance locoregional therapies for liver cancer treatment, we propose in this study a mathematical model to optimize the transcatheter arterial delivery of therapeutical agents. To maximize the effect of the treatment and minimize adverse effects on the patient, different mathematical models of the tumor growth are considered in this study to find the optimal number of the therapeutic drug-loaded magnetic nanoparticles to be administered. Three types of therapy models are considered, e.g., angiogenesis inhibition therapy, chemotherapy and radiotherapy. We use state-dependent Riccati equations (SDRE) as an optimal control methodology framework to the Hahnfeldts tumor growth formulation. Based on this, design optimal rules are derived for each therapy to reduce the growth of a tumor through the administration of appropriate dose of antiangiogenic, radio- and chemo-therapeutic agents. Simulation results demonstrate the validity of the proposed optimal delivery approach, leading to reduced intervention time, low drug administration rates and optimal targeted delivery.