Spatial signal distribution learning for high-resolution 3D system matrix calibration in magnetic particle imaging

Objective.Three-dimensional (3D) high-resolution system matrices (HR-SMs) are essential for high-quality image reconstruction in magnetic particle imaging (MPI), but obtaining HR-SMs is time-consuming and costly. This study aims to develop a learning-based 3D SM calibration method to reduce the calibration workload and maintain reconstruction accuracy.Approach.We propose a spatial signal distribution learning method for fast calibration of 3D HR-SMs. Specifically, a channel-decoupled multi-path state space model (MPC-SSM) is designed. This method flattens the 3D SM into multiple complementary spatial sequences, and uses different traversal paths to capture anisotropy and long-range spatial dependencies. To improve efficiency, feature channels are divided into disjoint groups and assigned to path-specific SSMs, achieving efficient multi-path sequence modeling while reducing computational overhead.Main results.We evaluated this method on both simulated and real MPI datasets (including OpenMPI). The results show that underandupsampling, the normalized reconstruction error of MPC-SSM is lower than that of existing interpolation and deep learning methods, and the quality of downstream image reconstruction is improved.Significance.This work provides a scalable and practical solution for 3D HR-SM calibration and provides a general modeling strategy for structured 3D medical data.