PP-MPI: A deep plug-and-play prior for magnetic particle imaging reconstruction

Abstract

Magnetic particle imaging (MPI) is a recent modality that enables high contrast and frame-rate imaging of the magnetic nanoparticle (MNP) distribution. Based on a measured system matrix, MPI reconstruction can be cast as an inverse problem that is commonly solved via regularized iterative optimization. Yet, hand-crafted regularization terms can elicit suboptimal performance. Here, we propose a novel MPI reconstruction “PP-MPI” based on a deep plug-and-play (PP) prior embedded in a model-based iterative optimization. We propose to pre-train the PP prior based on a residual dense convolutional neural network (CNN) on an MPI-friendly dataset derived from magnetic resonance angiograms. The PP prior is then embedded into an alternating direction method of multiplier (ADMM) optimizer for reconstruction. A fast implementation is devised for 3D image reconstruction by fusing the predictions from 2D priors in separate rectilinear orientations. Our demonstrations show that PP-MPI outperforms state-of-the-art iterative techniques with hand-crafted regularizers on both simulated and experimental data. In particular, PP-MPI achieves on average 3.10 dB higher peak signal-to-noise ratio than the top-performing baseline under variable noise levels, and can process 12 frames/sec to permit real-time 3D imaging.