Structure-Constrained System Matrix Extrapolation for Boundary Artifact Suppression in Multipatch Magnetic Particle Imaging

Magnetic particle imaging (MPI) is an emerging molecular imaging modality that often relies on multipatch scanning to increase the field of view (FOV). Conventional multipatch reconstruction typically requires system matrices (SMs) measured not only inside the FOV but also in surrounding overscan regions to suppress boundary artifacts, which leads to an additional calibration burden, especially in three-dimensional imaging. In this work, we propose a structure-constrained deep learning framework for SM extrapolation using measurements acquired only within the FOV. Three MPI-motivated structural regularizers, namely, local smoothness, gradient-domain regularity, and reflection-equivariance consistency, are incorporated into training. In particular, in the reflection-equivariance term, a dual-input mechanism is used to reduce the spurious sensitivity of the extrapolation mapping to reflection-induced changes in the input orientation, thereby promoting stable and structurally plausible SM extrapolation. Furthermore, a hybrid Uformer architecture with coordinate-aware spatial position embedding and transformer-based skip fusion is used to model both local spatial structure and long-range feature interactions. The results of experiments on simulation, OpenMPI, phantom, and in vivo datasets reveal that the proposed method effectively suppresses boundary artifacts and improves reconstruction fidelity over the evaluated baselines. By avoiding overscan calibration, the proposed framework reduces the number of calibrated SM samples required by 55.6%–81.7% in the studied settings while maintaining competitive reconstruction quality, which suggests a promising calibration-efficient strategy for large-FOV MPI.