Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, MPI, FPGA, Deep Learning, Reconstruction
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.
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