Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model

Abstract

Targeted delivery of magnetic nanoparticles (MNPs) to an area of a blood vessel with fluidic flow is hampered by the lack of a suitable real‐time imaging modality for MNPs, the control system complexity, and low targeting performance. Herein, an offline programming guidance (OLPG) scheme for aggregated MNPs is proposed based on a real‐time aggregation volume estimator. The proposed aggregation volume estimator based on a magnetic drug‐targeting simulator reflects volume changes of aggregated MNPs; hence, it can model a magnetic force acting on aggregated MNPs in real time while enhancing targeting performance. The proposed guidance system is evaluated using a simulation testbed and in vitro model of the rat brain, which yields comparable results at different fluid viscosities, flow velocities, target areas, and flow types. The OLPG with the aggregation volume estimator improves targeting performance by 116%–409% compared with the default mode, and by 111%–180% compared to the performance without the aggregation volume estimator. Furthermore, a guidance margin predicts enhanced targeting performance (root‐mean‐square error < 5%) irrespective of the flow environment. The proposed guidance strategy has the potential to overcome the problems caused by the lack of an imaging modality, control‐system complexity, and low targeting performance.

Keywords

• aggregation model
• aggregation volume
• guidance margin
• magnetic drug targeting
• micro/nanorobotics