Simulations and Advancements in MRI-Guided Power-Driven Ferric Tools for Wireless Therapeutic Interventions

TL;DR

This work tackles the challenge of precise, safe intravascular interventions inside an MRI by developing an integrated computational system that maps vascular networks from MR images and guides a tetherless, spherical MRbot using MRI gradient forces. The approach combines 0D/1D/3D hemodynamic modeling, PCHIP-based data estimation, and a PID-based control loop with virtual fixtures to achieve closed-loop, real-time navigation within pulsatile blood flow. Key contributions include a detailed gradient-driven propulsion framework, explicit trajectory and velocity setpoints, and visualization pipelines (VTK/ParaView/Qt) that respect FDA gradient limits while enabling accurate path-following in complex vasculature. The findings advance MRI-guided robotic interventions by enabling precise, safe navigation through vascular networks and lay the groundwork for ML-enabled performance prediction and immersive training tools.

Abstract

Designing a robotic system that functions effectively within the specific environment of a Magnetic Resonance Imaging (MRI) scanner requires solving numerous technical issues, such as maintaining the robot's precision and stability under strong magnetic fields. This research focuses on enhancing MRI's role in medical imaging, especially in its application to guide intravascular interventions using robot-assisted devices. A newly developed computational system is introduced, designed for seamless integration with the MRI scanner, including a computational unit and user interface. This system processes MR images to delineate the vascular network, establishing virtual paths and boundaries within vessels to prevent procedural damage. Key findings reveal the system's capability to create tailored magnetic field gradient patterns for device control, considering the vessel's geometry and safety norms, and adapting to different blood flow characteristics for finer navigation. Additionally, the system's modeling aspect assesses the safety and feasibility of navigating pre-set vascular paths. Conclusively, this system, based on the Qt framework and C/C++, with specialized software modules, represents a major step forward in merging imaging technology with robotic aid, significantly enhancing precision and safety in intravascular procedures.

Figures (7)

• Figure 1: Outcomes for Tp intervals of 100ms (depicted in blue) and 200ms (shown in red) during the simulation of steady blood flow are presented. Figure (a) illustrates the curvature of the path. Figure (b) displays the steady blood flow pattern. Figures (c), (d), and (e) detail the gradient fields produced by the MRI scanner for each respective axis.
• Figure 2: Findings for Tp values of 100ms (in blue) and 200ms (in red) from the simulation under normal blood flow conditions are shown. Figure (a) depicts the curvature of the path. Figure (b) demonstrates the normal blood flow. Figures (c), (d), and (e) display the gradient fields created by the MRI scanner for each axis.
• Figure 3: The outcomes from the simulation at Tp intervals of 100ms (illustrated in blue) and 200ms (represented in red) under fast heart rate blood flow conditions are detailed. Figure (a) charts the path's curvature. Figure (b) exhibits the blood flow at a fast heart rate. Figures (c), (d), and (e) outline the gradient fields produced by the MRI scanner for each respective axis.
• Figure 4: Outcomes for Tp values of 100ms (represented in blue) and 200ms (illustrated in red) were observed in the simulation under steady blood flow conditions. Figures (a), (b), and (c) display the dB/dt rate for each respective axis, with a blood flow rate of 1ml/s.
• Figure 5: Outcomes from the simulation at Tp settings of 100ms (depicted in blue) and 200ms (shown in red) during normal blood flow conditions were recorded. Charts (a), (b), and (c) depict the dB/dt values for each respective axis in these tests.