A two-step regularization reconstruction algorithm for magnetic particle imaging

Magnetic particle imaging (MPI) is an imaging technique used to determine the spatial concentration distribution of superparamagnetic nanoparticles. Tikhonov regularization algorithm is a commonly used reconstruction algorithm in MPI, but the reconstruction accuracy of this method is low, especially when the concentration distribution of magnetic nanoparticles in the image region is widely different, its image quality is difficult to meet the imaging requirements of particle spatial concentration distribution. In this paper, a two-step regularized magnetic particle imaging algorithm is proposed. Firstly, the signal of high concentration particles is extracted and the Tikhonov reconstruction is performed in the first step to obtain the distribution image of high concentration particles. Then, the second step of Tikhonov reconstruction was performed to obtain the low-concentration particle distribution image. Finally, high and low concentration particle distribution images are fused to achieve high quality image of particle concentration distribution. The simulation results show that the maximum concentration ratio of the two samples in MPI is increased by 16 times, and the signal to artifact (SAR) ratio is increased by 16 times. Therefore, the proposed two-step regularization reconstruction algorithm has a good reconstruction effect for magnetic particle imaging with large concentration difference distribution.