Current reconstruction approaches of magnetic particle imaging: A review

As a non-invasive imaging technique, Magnetic Particle Imaging (MPI) relies on the detection of superparamagnetic nanoparticles to visualize biological processes with exceptional sensitivity and temporal–spatial resolution. Image reconstruction plays a crucial role in MPI, as it involves converting induced voltage signals into detailed images of the concentration distribution of superparamagnetic iron oxide particles, and the accuracy and efficiency of the reconstruction algorithm significantly affect the quality of the reconstructed image. This review introduces the research status of MPI reconstruction, and then focuses on the basic theories and improved algorithms of reconstruction methods developed in recent years, including traditional system matrix (SM) based and x-space based methods and deep learning (DL)-based strategies. In addition, the advantages and limitations of each method are discussed, along with comparative analysis. By offering insights into the various MPI reconstruction methods, this review aims to serve as a valuable reference for researchers working in this field. It is hoped that this comprehensive overview will contribute to the future development of MPI in clinical medicine. •A review article is presented on known methods for Magnetic Particle Imaging(MPI) reconstruction.•The main advantages and disadvantages of existing technologies are considered.•The existing groundwork and future prospects are described.•All publications show great promise for MPI in medical imaging.