Abstract: 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 achie

Abstract: A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results.