Partial FOV Center Imaging (PCI): A Robust X-Space Image Reconstruction for Magnetic Particle Imaging

TL;DR

This paper introduces PCI, a robust x-space image reconstruction method for MPI that simplifies processing and improves image quality and noise resilience when dealing with partial FOVs.

Contribution

The paper presents PCI, a novel MPI reconstruction technique that directly maps signals to FOV centers and deconvolves to produce high-quality images with enhanced robustness.

Findings

PCI outperforms standard methods in noise robustness.

PCI demonstrates improved image quality in experiments.

The method effectively handles non-ideal signal conditions.

Abstract

Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles using their nonlinear response to applied magnetic fields. In standard x-space approach to MPI, the image is reconstructed by gridding the speed-compensated nanoparticle signal to the instantaneous position of the field free point (FFP). However, due to safety limits on the drive field, the field-of-view (FOV) needs to be covered by multiple relatively small partial field-of-views (pFOVs). The image of the entire FOV is then pieced together from individually processed pFOVs. These processing steps can be sensitive to non-ideal signal conditions such as harmonic interference, noise, and relaxation effects. In this work, we propose a robust x-space reconstruction technique, Partial FOV Center Imaging (PCI), with substantially simplified pFOV processing. PCI first forms a raw image of the entire FOV by mapping MPI signal directly to pFOV center locations. The corresponding MPI image is then obtained by deconvolving this raw image by a fully known, compact kernel, which solely depends on the pFOV size. We analyze the performance of the proposed reconstruction via extensive simulations, as well as imaging experiments on our in-house FFP MPI scanner. The results show that PCI offers a trade-off between noise robustness and interference robustness, outperforming standard x-space reconstruction in terms of both robustness against non-ideal signal conditions and image quality.