In remote magnetic navigation, a magnetic navigation system is used to generate magnetic fields to apply mechanical wrenches to steer a magnetic object. This technique can be applied to navigate untethered micro- and nanorobots, as well as tethered magnetic surgical tools for minimally invasive medicine. The design and characterization of these systems have been extensively investigated over the past decade. The determination of the region in space in which these systems can operate has yet to be formalized within the research community. This region is commonly referred to as the “workspace” and constitutes a central concept for any class of robotic system. We focus on magnetic navigation systems comprised of electromagnets and propose a first set of definitions for a magnetic workspace, a methodology to determine it, and evaluation metrics to analyze its characteristics. Our methodology and tools are illustrated with several examples of planar and spatial electromagnetic magnetic navigation systems for both didactic and realistic navigation scenarios.

Remote magnetic navigation is used for the manipulation of untethered micro and nanorobots, as well as tethered magnetic surgical tools for minimally invasive medicine. Mathematical modeling of the magnetic fields generated by magnetic navigation systems is a fundamental task in the control of such tools for biomedical applications. In this article, we describe and compare several existing and newly developed methods for representations of continuous magnetic fields using interpolation in the context of remote magnetic navigation. Clinical-scale electromagnetic navigation systems feature nonlinear magnetization and magnetization interactions between electromagnets, which renders accurate magnetic field modeling challenging. We first introduce a method that can adapt existing linear models to correct for nonlinear magnetization, with similar performance to the current state-of-the-art nonlinear model. Furthermore, we present a method based on convolutional neural networks.

Electromagnetic Navigation Systems (eMNS) can be used to control a variety of multiscale devices within the human body for remote surgery. Accurate modeling of the magnetic fields generated by the electromagnets of an eMNS is crucial for the precise control of these devices. Existing methods assume a linear behavior of these systems, leading to significant modeling errors within nonlinear regions exhibited at higher magnetic fields, preventing these systems from operating at full capacity. In this paper, we use a random forest (RF) and an artificial neural network (ANN) to model the nonlinear behavior of the magnetic fields generated by an eMNS. Both machine learning methods outperformed the state-of-the-art linear multipole electromagnet model (MPEM). The RF and the ANN model reduced the root mean squared error (RMSE) of the MPEM when predicting the field magnitude by approximately 40% and 87%, respectively, over the entire current range of the eMNS. At high current regions, especially between 30 and 35 A, the field-magnitude RMSE improvement of the ANN model over the MPEM was 37 mT, equivalent to 90% error reduction. This study demonstrates the feasibility of using machine learning to model an eMNS for medical applications, and its ability to account for complex nonlinear behavior at high currents. The use of machine learning thus shows promise in developing accurate field predicting models, and ultimately improving surgical procedures that use magnetic navigation.

Cardiac arrhythmias are commonly treated by minimally invasive catheter ablation. Because of challenges in precisely manipulating the distal end of the catheter, several magnetic manipulation systems (MMS) have been developed to aid in steering the tip inside the heart. Due to the relative stiffness of catheters, magnetic gradients that create forces acting on the catheter tip position are limited in their effectiveness and are typically ignored. This dramatically limits the tip orientations possible at any specified position in the workspace. Replacing the flexible catheter tip by a string-like tether and replacing multiple magnets by a single magnet allows the ablation tool to be positioned at any location with essentially any orientation. In addition to the five pose degrees of freedom, the tension on the tether can also be controlled. During an ablation procedure, the distal end of the tethered magnet is placed in contact with the heart wall. Extending the tether after tip contact transfers the tension on the tether to a force at the contact point. The contact force can then be precisely controlled and is no longer dependent on catheter bending radius and insertion length, as is the case with current catheter steering systems. The kinematics of a tethered magnet is modeled and tested inside a clinical MMS called the Aeon Phocus. This demonstrates the feasibility of magnetic field gradient control in a medically certified system currently in clinical use.

Remote magnetic navigation of catheters is a technique used to perform radiofrequency ablation of heart tissue in order to treat cardiac arrhythmias. The flexible magnetic catheters used in this context are in some cases not sufficiently dexterous to navigate the complex and patient-specific anatomy of the heart. To overcome such limitations, this paper proposes a new approach that relies on the integration of variable stiffness segments into a magnetic catheter. The magnetic variable stiffness (VS) catheter presented here is based on silicone and a low melting point alloy (LMPA) that transforms from a solid to liquid phase upon joule heating. This dramatically changes the bending stiffness of the segment in which it is integrated, improving dexterity. Compared to standard catheters, a VS catheter can partially (just one segment) or completely lock its shape (shape fixity) in order to explore a larger 3D volume inside a magnetic navigation system, thus extending regions of the heart that can be reached for performing ablation procedures.