Simulations of MRI Guided and Powered Ferric Applicators for Tetherless Delivery of Therapeutic Interventions

TL;DR

This paper presents a computational platform that models MRI-powered ferromagnetic applicators for safe, tetherless delivery of therapeutic interventions inside blood vessels, integrating preoperative planning, vessel modeling, and safety constraints.

Contribution

It introduces a novel integrated simulation platform that combines MRI data processing, virtual corridor creation, and magnetic field waveform generation for planning MRI-powered vascular interventions.

Findings

Successfully models vascular pathways and safety constraints.

Generates magnetic field waveforms tailored to vessel geometry.

Provides real-time simulation capabilities for planning.

Abstract

Magnetic Resonance Imaging (MRI) is a well-established modality for pre-operative planning and is also explored for intra-operative guidance of procedures such as intravascular interventions. Among the experimental robot-assisted technologies, the magnetic field gradients of the MRI scanner are used to power and maneuver ferromagnetic applicators for accessing sites in the patient's body via the vascular network. In this work, we propose a computational platform for preoperative planning and modeling of MRI-powered applicators inside blood vessels. This platform was implemented as a two-way data and command pipeline that links the MRI scanner, the computational core, and the operator. The platform first processes multi-slice MR data to extract the vascular bed and then fits a virtual corridor inside the vessel. This corridor serves as a virtual fixture (VF), a forbidden region for the applicators to avoid vessel perforation or collision. The geometric features of the vessel centerline, the VF, and MRI safety compliance (dB/dt, max available gradient) are then used to generate magnetic field gradient waveforms. Different blood flow profiles can be user-selected, and those parameters are used for modeling the applicator's maneuvering. The modeling module further generates cues about whether the selected vascular path can be safely maneuvered. Given future experimental studies that require a real-time operation, the platform was implemented on the Qt framework (C/C++) with software modules performing specific tasks running on dedicated threads: PID controller, generation of VF, generation of MR gradient waveforms.