# Fast Trajectory-Independent Model-Based Reconstruction Algorithm for Multi-Dimensional Magnetic Particle Imaging

arXiv: https://arxiv.org/abs/2505.22797 · HTML: https://mlsanity.com/paper/2505.22797/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 7 of 7)

- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [DEQ-MPI: A Deep Equilibrium Reconstruction with Learned Consistency for Magnetic Particle Imaging](https://mlsanity.com/paper/2212.13233/) (2022)
- [Model-Based Reconstruction for Magnetic Particle Imaging in 2D and 3D](https://mlsanity.com/paper/1605.08095/) (2016)
- [Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset](https://mlsanity.com/paper/2007.01593/) (2020)
- [An ℓ1-Plug-and-Play Approach for MPI Using a Zero Shot Denoiser with Evaluation on the 3D Open MPI Dataset](https://mlsanity.com/paper/2401.00275/) (2023)
- [Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging](https://mlsanity.com/paper/2403.00602/) (2024)
- [Efficient Chebyshev Reconstruction for the Anisotropic Equilibrium Model in Magnetic Particle Imaging](https://mlsanity.com/paper/2504.12981/) (2025)

## Built on by (most cited first, 1 of 1)

- [MPISuperRes-PnP: A Super-Resolution Zero-Shot Plug-and-Play Reconstruction Algorithm for Magnetic Particle Imaging](https://mlsanity.com/paper/2608.09672/) (2026)

## Abstract (arXiv)

Magnetic Particle Imaging (MPI) is a promising tomographic technique for visualizing the spatio-temporal distribution of superparamagnetic nanoparticles, with applications ranging from cancer detection to real-time cardiovascular monitoring. Traditional MPI reconstruction relies on either time-consuming calibration (measured system matrix) or model-based simulation of the forward operator. Recent developments have shown the applicability of Chebyshev polynomials to multi-dimensional Lissajous Field-Free Point (FFP) scans. This method is bound to the particular choice of sinusoidal scanning trajectories. In this paper, we present the first reconstruction on real 2D MPI data with a trajectory-independent model-based MPI reconstruction algorithm. We further develop the zero-shot Plug-and-Play (PnP) algorithm of the authors -- with automatic noise level estimation -- to address the present deconvolution problem, leveraging a state-of-the-art denoiser trained on natural images without retraining on MPI-specific data. We evaluate our method on the publicly available 2D FFP MPI dataset ``MPIdata: Equilibrium Model with Anisotropy", featuring scans of six phantoms acquired using a Bruker preclinical scanner. Moreover, we show reconstruction performed on custom data on a 2D scanner with additional high-frequency excitation field and partial data. Our results demonstrate strong reconstruction capabilities across different scanning scenarios -- setting a precedent for general-purpose, flexible model-based MPI reconstruction.