A model-based direct inversion network (MDIN) for dual spectral computed tomography

Abstract

Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Additionally, several existing DSCT methods rely heavily on the information of the spectra, which is often not readily available in applications. To address this problem, in this study, we propose a Model-based Direct Inversion Network (MDIN) for DSCT, which can directly predict the basis material images from the collected polychromatic projections. The all operations are performed in the network, requiring
neither the conventional algorithms nor the information of the spectra. It can be viewed as an approximation to the inverse procedure of DSCT imaging model. The MDIN is composed of projection pre-decomposition module (PD-module), domain transformation layer (DT-layer), and image post-decomposition module (ID-module). The PD-module first performs the pre-decomposition on the polychromatic projections that consists of a series of stacked one-dimensional convolution layers. The DT-layer is designed to obtain the preliminary decomposed results, which has the characteristics of sparsely connected and learnable parameters. And the ID-module uses a deep neural network (DNN) to further decompose the reconstructed results of the DT-layer so as to achieve higher-quality basis material images. Numerical experiments demonstrate that the proposed MDIN has significant advantages in substance decomposition,
artifact reduction and noise suppression compared to other methods in the DSCT reconstruction.