Tabletop Magnetic Particle Imaging Using Deep FPGA-based Convolutional Neural Network

Motivation: When using complex prior parameters for regularization in the MPI reconstruction method based on the system matrix, the process is very time-consuming and the preprocessing process is complex. Goal(s): The purpose of this study is to simplify the reconstruction process and achieve efficient real-time reconstruction of magnetic particle images. Approach: Therefore, this study uses CNN neural network for reconstruction on FPGA, and tests the neural network reconstruction effect through simulation and customized MPI system(Fig. 1). Results: The results show that it is feasible to use CNN neural network for reconstruction on FPGA, achieving high efficiency and real-time performance of magnetic particle image reconstruction. Impact: FPGA-based CNN reconstruction network can make desktop magnetic particle imaging easier and more efficient.