Xidian University

Shenghan Ren

Shouping Zhu

Xueli Chen

Duofang Chen

Jie Tian

Hui Xie

Pengfei Huang

In a magnetic particle imaging reconstruction method based on a neural network constrained by a forward model, a system matrix is obtained through calibration, voltage data generated by a specimen is measured, collected data are transformed into a frequency-domain by Fourier transform, frequency features of data are screened by a signal-to-noise ratio threshold, a reconstruction network is built by a Pytorch to realize mapping from one-dimensional voltage data to a multi-dimensional magnetic particle concentration distribution, the system matrix is used as the forward model of magnetic particle imaging, and simulated voltage data is generated according to a reconstructed multi-dimensional magnetic particle concentration distribution, a difference between the simulated voltage data and input voltage data is calculated as a loss function for network parameter updating, a total variation regularization term is added to the loss function, and training parameters and regularization parameters are adjusted to achieve an optimal reconstruction effect.

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Xi'an, China

Shenghan Ren