Modeling Real-World MPI System Matrices from Sparse Observations

Magnetic Particle Imaging (MPI) offers unique advantages, including high sensitivity, real-time imaging, and absence of ionizing radiation. However, the prevailing system matrix (SM)-based reconstruction in MPI faces critical limitations: time-consuming calibration, noise vulnerability, and reliance on high-resolution training data. To overcome these challenges, we propose an imaging physics driven neural field framework for efficient SM calibration and robust reconstruction. Key innovations include: (1) First-order derivative constraints to suppress spiky noise, (2) An M-order separable representation to enforce smoothness and reduce fluctuation artifacts, and (3) Chebyshev polynomial integration to enhance encoding efficiency and embed imaging physics priors. The method adapts to variable resolution requirements, reduces dependency on high-resolution data, and demonstrates robustness to noise across diverse SNR conditions. Experiments on the OpenMPI dataset show remarkable performance, achieving 1.55% nRMSE at 25% sparsity and minimal 0.21% degradation at 6.25% sparsity. Furthermore, upsampling sparse internal MPI system via the proposed method successfully reconstructs phantom geometries with high fidelity. These results validate the method's potential to advance MPI toward broader research applications.