A Progressive Multidimensional Particle Swarm Optimizer for magnetic core placement in Dipole Field Navigation

Abstract

This paper tackles the inverse problem of finding optimal configurations of magnetic gradient sources in Dipole Field Navigation (DFN), a magnetic navigation method proposed recently for the direct targeting of drugs. In DFN, a limited number of these gradient sources, called the cores, must be positioned properly around a patient in a Magnetic Resonance Imaging scanner to induce the required directional forces on the navigated therapeutic carriers. To overcome some limitations of the previous approach for solving this problem, here we propose a novel and conceptually simple multidimensional variant of the well-known Particle Swarm Optimization (PSO) algorithm. This variant, called Progressive Multidimensional PSO (PMD-PSO), enables a tradeoff between the quality and the complexity of the solutions by progressively increasing the number of dimensions in the search space. We apply this algorithm to the core placement problem using an improved fitness function for the evaluation of a core configuration given a vascular path towards a target. Experiments on simulated vasculatures show that, while the approach can effectively solve this inverse problem, PMD-PSO exhibits better performances for DFN compared with two other multidimensional PSO variants.