Simultaneous Imaging of Widely Differing Particle Concentrations in MPI: Problem Statement and Algorithmic Proposal for Improvement

TL;DR

This paper proposes a two-step algorithm for Magnetic Particle Imaging that significantly enhances the dynamic range and allows adaptive regularization, improving image quality for samples with widely differing particle concentrations.

Contribution

It introduces a novel two-step algorithm that increases MPI dynamic range and enables spatially adaptive regularization for better image reconstruction.

Findings

Increases MPI dynamic range by a factor of four.

Enables spatially adaptive regularization for improved resolution.

Effectively reconstructs images with inhomogeneous particle distributions.

Abstract

Magnetic Particle Imaging (MPI) is a tomographic imaging technique for determining the spatial distribution of superparamagnetic nanoparticles. Current MPI systems are capable of imaging iron masses over a wide dynamic range of more than four orders of magnitude. In theory, this range could be further increased using adaptive amplifiers, which prevent signal clipping. While this applies to a single sample, the dynamic range is severely limited if several samples with different concentrations or strongly inhomogeneous particle distributions are considered. One scenario that occurs quite frequently in pre-clinical applications is that a highly concentrated tracer bolus in the vascular system "shadows" nearby organs with lower effective tracer concentrations. The root cause of the problem is the ill-posedness of the MPI imaging operator, which requires regularization for stable reconstruction. In this work, we introduce a simple two-step algorithm that increases the dynamic range by a factor of four. Furthermore, the algorithm enables spatially adaptive regularization, i.e. highly concentrated signals can be reconstructed with maximum spatial resolution, while low concentrated signals are strongly regularized to prevent noise amplification.