Reconstruction of 1D Images with a Neural Network for Magnetic Particle Imaging

Image reconstruction in Magnetic Particle Imaging is mainly performed by using a system matrix or by mapping the time signal into spatial domain and deconvolving the tracer properties. In this work, a neural network is designed and trained for reconstructing 1D images. Test data are reconstructed using both the neural network and a conventional approach. Background artefacts that appear during conventional reconstruction are not visible when reconstructing with the neural network. The images that have been reconstructed using the neural network are superior in terms of quantifiability and spatial resolution in comparison to conventionally reconstructed images.