Matthieu Fruchard

MRI-Guided Nanorobotic Systems for Therapeutic and Diagnostic Applications

This review presents the state of the art of magnetic resonance imaging (MRI)-guided nanorobotic systems that can perform diagnostic, curative, and reconstructive treatments in the human body at the cellular and subcellular levels in a controllable manner. The concept of an MRI-guided nanorobotic system is based on the use of an MRI scanner to induce the required external driving forces to propel magnetic nanocapsules to a specific target. It is an active targeting mechanism that provides simultaneous propulsion and imaging capabilities, which allow the implementation of real-time feedback control of the targeting process. The architecture of the system comprises four main modules: (a) the nanocapsules, (b) the MRI propulsion module, (c) the MRI tracking module (for image processing), and (d) the controller module. A key concept is the nanocapsule technology, which is based on carriers such as liposomes, polymer micelles, gold nanoparticles, quantum dots, metallic nanoshells, and carbon nanotubes. Descriptions of the significant challenges faced by the MRI-guided nanorobotic system are presented, and promising solutions proposed by the involved research community are discussed. Emphasis is placed on reviewing the limitations imposed by the scaling effects that dominate within the blood vessels and also on reviewing the control algorithms and computational tools that have been developed for real-time propulsion and tracking of the nanocapsules.

Nonlinear modeling and robust controller-observer for a magnetic microrobot in a fluidic environment using MRI gradients

This paper reports the use of a MRI device to pull a magnetic microrobot inside a vessel and control its trajectory. The bead subjected to magnetic and hydrodynamic forces is first modeled as a nonlinear control system. Then, a backstepping approach is discussed in order to synthesize a feedback law ensuring the stability along the controlled trajectory. We show that this control law, combined with a high gain observer, provides good tracking performances and robustness to measurement noise as well as to some matched uncertainties.

Adaptive Controller and Observer for a Magnetic Microrobot

This paper discusses the control design of a magnetically guided microrobotic system in blood vessels to perform minimally invasive medical procedures. Such microrobots consist of a polymer-bonded aggregate of nanosized ferromagnetic particles and a possible payload that can be propelled by the gradient coils of a magnetic device. A fine modeling is developed and used to define an optimal trajectory which minimizes the control efforts. We then synthesize an adaptive backstepping law that ensures a Lyapunov stable and fine tracking, despite modeling errors, and estimates some key uncertain parameters. As the controller synthesis uses the microrobot unmeasured velocity, the design of a high-gain observer is also addressed. Simulations and experiment illustrate the robustness to both noise measurement and some uncertain physiological parameters for a 250-μm radius microrobot that navigates in a fluidic environment.

Adaptive backstepping and MEMS force sensor for an MRI-guided microrobot in the vasculature

A microrobot consisting of a polymer binded aggregate of ferromagnetic particles is controlled using a Magnetic Resonance Imaging (MRI) device in order to achieve targeted therapy. The primary contribution of this paper is the design of an adaptive backstepping controller coupled with a high gain observer based on a nonlinear model of a microrobot in a blood vessel. This work is motivated by the difficulty in accurately determining many biological parameters, which can result in parametric uncertainties to which model-based approaches are highly sensitive. We show that the most sensitive parameter, magnetization of the microrobot, can be measured using a Micro-Electro-Mechanical Systems (MEMS) force sensor, while the second one, the dielectric constant of blood, can be estimated on line. The efficacy of this approach is illustrated by simulation results.

Dynamic behavior investigation for trajectory control of a microrobot in blood vessels

This paper reports modeling and control of a microsized polymer aggregate of magnetic particles inside an artery, using a MRI device for supplying propulsion in order to achieve targeted chemotherapy. Non-Newtonian behavior of blood is considered, as well as wall effects and interactions, resulting in a highly nonlinear model. A backstepping approach is synthesized to ensure Lyapunov stability along a pre-planned trajectory inherited from the model, with robustness concerns.

Estimation of the Blood Velocity for Nanorobotics

This paper aims to estimate the blood velocity to enhance the navigation of an aggregate in the human vasculature. The considered system is a polymer-binded aggregate of ferromagnetic nanorobots immersed in a blood vessel. The drag force depends on the blood velocity and especially acts on the aggregate dynamics. In the design of advanced control laws, the blood velocity is usually assumed to be known or set to a constant mean value to achieve the control objectives. We provide theoretical tools to online estimate the blood velocity from the sole measurement of the aggregate position and combine the state estimator with a backstepping control law. Two state estimation approaches are addressed and compared through simulations: a high-gain observer and a receding horizon estimator. Simulations illustrate the efficiency of the proposed approach combining online estimation and control for the navigation of an aggregate of nanorobots.

Optimal trajectory for a microrobot navigating in blood vessels

The chemotherapy magnetically controlled under Magnetic Resonance Imaging (MRI) is currently one of the active areas of cancer research. This paper proposes a precise model of a therapeutic microrobot magnetically steered in blood vessels. This modeling approach takes into account the non-Newtonian behavior of blood, as well as wall effect on the bloods profile and robot-to-wall interaction forces. A backstepping approach law is used to ensure a null error between the real trajectory and an optimal reference trajectory deduced from the highly nonlinear model. The strengths and limitations of the overall study are evaluated by simulations.

High Gain Observer for backstepping control of a MRI-guided therapeutic microrobot in blood vessels

This paper reports precise modeling and controller/ observer design for a microsized polymer aggregate of magnetic particles inside an artery, using a Magnetic Resonance Imaging (MRI) device for supplying propulsion in order to achieve targeted chemotherapy. Non-Newtonian behaviour of blood is taken into account, as well as wall effects and interactions, resulting in a highly nonlinear model. A High Gain Observer is designed to reconstruct the unmeasured state, so that a backstepping controller can be synthesized. Efficiency and robustness to noise of this controller/observer pair is then illustrated through simulations results.

2D Observer-Based Control of a Vascular Microrobot

The paper addresses the 2D observer-based control of a magnetic microrobot navigating in a cylindrical blood vessel along a reference trajectory. In particular, this robot faces the nonlinear drag force induced by the pulsatile blood flow, which can hardly be measured. We consequently propose a mean value theorem (MVT) based observer to estimate the blood velocity from the sole measurement of the robot position. We also prove the stability of the observer-based backstepping controller. The resulting estimation and tracking are then illustrated through simulations, as well as robustness to parametric uncertainty, measurement noise, and dynamical errors when the pulsatile blood flow is incorrectly modeled.

Observer-based controller for microrobot in pulsatile blood flow

We propose an observer-based controller for a magnetic microrobot immersed in the human vasculature. The drag force depends on the pulsatile blood velocity and specially acts on the microrobot dynamics. In the design of advanced control laws, the blood velocity is usually assumed to be known or set to a constant mean value to achieve the control objectives, whereas the sole robot position is measured. We prove the stability of the proposed observer-based controller combining a backstepping controller with a mean value theorem (MVT) based observer. The resulting estimation of the blood velocity is then illustrated and compared to high gain observer results through simulations.