Open AccessNeural Network Image Reconstruction for Magnetic Particle Imaging
Author(s) -
Chae Byung Gyu
Publication year - 2017
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2017-0094
Subject(s) - kernel (algebra) , convolution (computer science) , basis function , weighting , iterative reconstruction , artificial neural network , matrix (chemical analysis) , computer science , inverse , algorithm , chebyshev polynomials , artificial intelligence , network architecture , pattern recognition (psychology) , mathematics , mathematical analysis , physics , geometry , discrete mathematics , materials science , acoustics , composite material , computer security
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.