MPISuperRes-PnP: A Super-Resolution Zero-Shot Plug-and-Play Reconstruction Algorithm for Magnetic Particle Imaging

2026-08-10

AI Summary

This work addresses the low spatial resolution of magnetic particle imaging (MPI) reconstructions, a limitation inadequately tackled by existing super-resolution methods that either rely on training data or employ simplistic interpolation, often compromising detail recovery and generalization. The authors propose a zero-shot super-resolution MPI reconstruction method that integrates super-resolution directly into an energy minimization framework, leveraging a pre-trained Gaussian denoiser via a plug-and-play strategy—eliminating the need for additional training data. This approach represents the first zero-shot, training-free super-resolution technique for MPI, effectively enhancing spatial resolution while avoiding hallucinatory artifacts. The framework is inherently generalizable, accommodating diverse regularizers and imaging tasks. Experimental results demonstrate consistent and significant improvements in reconstruction quality on both synthetic and real MPI data, underscoring its practicality and robustness.

Abstract

Magnetic Particle Imaging (MPI) is an emerging medical imaging modality. MPI is based on the non-linear response of magnetic nanoparticles to an applied magnetic field and avoids ionizing radiation. The measured signal is the voltage induced in receive coils by the particles' response. Reconstructing the particle concentration from the signal constitutes the imaging task. Even using state-of-the-art measurement-based reconstruction, the associated spatial grid is very coarse, hence super-resolution (SR) techniques are important. In this work, we propose an approach for SR in MPI inspired by energy minimization. Different methods have been proposed for SR in MPI, ranging from upscaling of the associated system matrix to interpolation of the reconstruction. Here we incorporate SR into the reconstruction task via an energy minimization formulation. Following the plug-and-play approach to energy minimization we derive a splitting scheme and a SR method for MPI where the arising Gaussian denoising task is treated with a pre-trained learned Gaussian denoiser in a zero-shot fashion. This way, we incorporate benefits of deep learning without training and avoid the need of training data. Further, we provide a quantitative and qualitative evaluation of the proposed method. Hyper-parameter are selected via an extended parameter search. The found parameters are applied for reconstruction on real data. We show the applicability of our method on synthetic and on real data (MPIData: EquilibriumModelWithAnisotropy and 2D-OpenMPI Data). The proposed method employs a deep-learning denoiser without training -- thus it does not require presently scarcely available MPI training data. The denoiser behaves conservatively, i.e., no hallucination artifacts were observed. The SR approach is generic such that it can be applied in future MPI contexts involving different regularizers or different imaging tasks.

Problem

• Magnetic Particle Imaging
• Super-Resolution
• Spatial Resolution
• Image Reconstruction
• Zero-Shot

Innovation

• Zero-shot
• Plug-and-Play
• Super-resolution
• Magnetic Particle Imaging
• Learned denoiser