PARNet: A High-Quality Reconstruction Algorithm for Single-Harmonic Projection Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is an emerging quantitative imaging modality that exploits the nonlinear magnetization response of superparamagnetic nanoparticles, offering high sensitivity and real-time visualization for biomedical applications. Field-free line (FFL) scanning improves signal-to-noise ratio and acquisition efficiency but introduces severe challenges in image reconstruction. Conventional system matrix (SM)-based reconstruction methods achieve high accuracy but suffer from extensive calibration burdens, while X-space methods enable rapid reconstruction yet remain vulnerable to FFL nonuniformity and information loss, leading to blurring and artifacts. To address these limitations, this work proposes a Progressive Artifact Removal Network (PARNet) for FFL-based MPI reconstruction. The proposed algorithm integrates stage-wise artifact suppression, multi-dimensional attention modeling, and a noise-aware mechanism to progressively eliminate complex artifacts while preserving fine structural details. In support of this framework, a simulation platform with parameters consistent with our in-house MPI prototype is developed to generate large-scale, diverse datasets for efficient network training, eliminating the need for time-consuming SM calibration and mitigating reconstruction errors caused by the nonuniformity of magnetic fields along the FFL direction and filtered back-projection limitations. Comprehensive evaluations on both simulated and experimental data demonstrate that the proposed method outperforms existing approaches such as Deconv and SwinIR, achieving superior reconstruction quality with reduced blur and artifacts, accurate structural preservation, and efficient recovery from voltage signals to quantitative concentration images.