An ${\ell }^1$-Plug-and-Play Approach for MPI Using a Zero Shot Denoiser with Evaluation on the 3D Open MPI Dataset

Abstract

Objective: Magnetic particle imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a plug-and-play approach based on a generic zero-shot denoiser with an ${\ell }^1$-prior. Approach: We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, DIP and the previous PP-MPI, which is a plug-and-play method with denoiser trained on MPI-friendly data. Main results: We offer a quantitative and qualitative evaluation of the zero-shot plug-and-play approach on the 3D Open MPI dataset. Moreover, we show the quality of the approach with different levels of preprocessing of the data. Significance: The proposed method employs a zero-shot denoiser which has not been trained for the MPI task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.

24 pages, 7 figures, additional supplementary material (78 pages total)

References in corpus (9)

• Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
• Human-sized Magnetic Particle Imaging for Brain Applications
• Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
• Plug and play methods for magnetic resonance imaging (long version)
• An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
• Numerical Reconstruction in Magnetic Particle Imaging
• A new 3D model for magnetic particle imaging using realistic magnetic field topologies for algebraic reconstruction
• On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging
• Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging

Cited by in corpus (2)

• High-Resolution Magnetic Particle Imaging System Matrix Recovery Using a Vision Transformer with Residual Feature Network
• Fast Trajectory-Independent Model-Based Reconstruction Algorithm for Multi-Dimensional Magnetic Particle Imaging