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http://dx.doi.org/10.4218/etrij.2017-0094

Neural Network Image Reconstruction for Magnetic Particle Imaging  

Chae, Byung Gyu (SW.Contents Research Laboratory, ETRI)
Publication Information
ETRI Journal / v.39, no.6, 2017 , pp. 841-850 More about this Journal
Abstract
We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends 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 consisting 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 low convolution effects, as well as a nonlinear activation function, plays a key 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. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.
Keywords
Convolution effects; Image reconstruction; Magnetic particle imaging; Neural network; System matrix;
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