69 research outputs found
Neural network image reconstruction for magnetic particle imaging
We investigate neural network image reconstruction for magnetic particle
imaging. The network performance depends strongly on the convolution effects of
the spectrum input data. The larger convolution effect appearing at a
relatively smaller nanoparticle size obstructs the network training. The
trained single-layer network reveals the weighting matrix consisted of a basis
vector in the form of Chebyshev polynomials of the second kind. The weighting
matrix corresponds to an inverse system matrix, where an incoherency of basis
vectors due to a low convolution effects as well as a nonlinear activation
function plays a crucial role in retrieving the matrix elements. Test images
are well reconstructed through trained networks having an inverse kernel
matrix. We also confirm that a multi-layer network with one hidden layer
improves the performance. The architecture of a neural network overcoming the
low incoherence of the inverse kernel through the classification property will
become a better tool for image reconstruction.Comment: 9 pages, 11 figure
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