🤖 AI Summary
This work addresses the challenge of safely and precisely delivering untethered ferromagnetic therapeutic devices through vascular networks under MRI guidance by developing a real-time simulation platform that integrates vascular geometry, hemodynamics, and MRI physical constraints. Leveraging preoperative MRI data, the platform automatically reconstructs patient-specific vascular structures and introduces a novel virtual fixture mechanism to enforce safety boundaries, thereby preventing vessel damage. It incorporates user-defined blood flow models and accounts for MRI gradient field limitations to enable high-fidelity device navigation simulation. The system supports multithreaded real-time control, evaluates path feasibility, and generates gradient field commands compliant with MRI safety standards, offering reliable preoperative validation and operational guidance for subsequent in vivo experiments.
📝 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.