Cross-Axis Weighted Harmonic Method: A Frequency-Domain Approach for Enhanced Resolution in Magnetic Particle Imaging

TL;DR

This paper introduces Cross-Axis Harmonic Analysis (CAHA), a novel frequency-domain technique that enhances resolution and noise suppression in Magnetic Particle Imaging by adaptively extracting high-SNR harmonics across multiple axes.

Contribution

The paper presents CAHA, a new signal processing method that improves MPI image resolution and robustness by leveraging directional harmonic analysis across a wide frequency range.

Findings

Achieved sub-millimeter resolution (0.8 mm FWHM at 85 kHz)

Reduced noise with nRMSE as low as 0.01

Improved resolution by up to 20% on real-world data

Abstract

Magnetic Particle Imaging (MPI) is a promising imaging modality that tracks magnetic nanoparticles (MNPs) to generate real time, high-resolution images. However, achieving an optimal balance between strong signal strength and sharp image clarity remains challenging. Higher drive field frequencies improve the signal-to-noise ratio (SNR), but also risk image blurring due to nanoparticle relaxation effects. To address this, we developed an end-to-end MPI simulation framework that models MNPs behavior, magnetic field dynamics, signal acquisition, and image reconstruction across a wide frequency range (20 to 85 kHz). Central to this framework is Cross-Axis Harmonic Analysis (CAHA), a novel, frequency-domain signal processing technique that adaptively extracts high-SNR harmonics from the x, y, and z directions for improved signal reconstruction. Using a simulated 3D vascular phantom, CAHA significantly enhanced image quality, achieving sub-millimeter resolution (0.8 mm FWHM at 85 kHz), strong noise suppression (nRMSE as low as 0.01), and structural fidelity (SSIM up to 0.94 at 55 kHz). The peak SNR reached 29.7 dB at 85 kHz. The signal processed with CAHA was also tested with other reconstruction methods; when combined with total variation regularization, CAHA achieved a pSNR of 37.91 dB. Evaluation on the Open MPI dataset further demonstrated up to 20% resolution improvement, confirming CAHA's robustness on real-world data. Although minor blurring was observed at the highest frequency due to relaxation, CAHA consistently maintained image clarity. By leveraging directional harmonic content rather than the full signal, CAHA sets a new benchmark for sharper, faster, and more robust MPI imaging.