Neural network image reconstruction for magnetic particle imaging

TL;DR

This paper explores neural network-based image reconstruction in magnetic particle imaging, highlighting how convolution effects influence training and demonstrating improved results with multi-layer networks.

Contribution

It introduces a neural network approach that accounts for convolution effects and shows that multi-layer networks enhance reconstruction performance.

Findings

Single-layer network reveals Chebyshev polynomial basis vectors.

Convolution effects hinder network training at smaller nanoparticle sizes.

Multi-layer networks improve image reconstruction quality.

Abstract

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.