A Virtual Monochromatic Imaging Method for Spectral CT Based on Wasserstein Generative Adversarial Network With a Hybrid Loss

Zaifeng Shi,Huilong Li,Qingjie Cao,Qixing Hu,Jinzhuo Li

doi:10.1109/access.2019.2934508

Abstract

Spectral computed tomography (CT) has become a popular clinical diagnostic technique because of its unique advantage in material distinction. Specifically, it can perform virtual monochromatic imaging to obtain accurate tissue composition with less beam hardening artifacts. It is an ill-posed problem that monochromatic images are acquired by material decomposition matrix, suffering from amplified noise due to various uncertain factors. Aiming at modeling spatial and spectral correlations, this paper proposes a Wasserstein generative adversarial network with a hybrid loss (WGAN-HL) for monochromatic imaging instead of voxel-by-voxel decomposition. A min-max concept about the optimal transport is introduced in WGAN to make a tradeoff between generated images and target images where the authenticity of data cannot be distinguished anymore by network. The hybrid loss focuses on the data distribution of the generated images and target images from voxel space together with feature space to meet clinical requirements. Thereby, the proposed network can generate robust monochromatic images with accurate decomposition at any energy, while identifying and removing noise and artifacts. The advantages of this method are demonstrated in CT value measurement, beam hardening, and metal artifacts removal. Simulations and real tests prove that the WGAN-HL method preserves the important tissue details with less noise and it can reconstruct more accurate CT value. Both qualitative and quantitative comparisons show that the network is superior to other monochromatic imaging method.

Highlights

X-ray computed tomography (CT) has become a powerful inspection tool for medical applications [1]
In conclusion, we have presented a WASSERSTEIN GENERATIVE ADVERSARIAL NETWORK (WGAN)-based method with hybrid loss to virtual monochromatic imaging and are excited for its good robustness
WGAN introduces an optimal method of data transport

Summary

INTRODUCTION

X-ray computed tomography (CT) has become a powerful inspection tool for medical applications [1]. Studies from Komlosi et al [14] and Guggenberger et al [15] showed that the high-energy monochromatic images can reduce artifacts caused by metal implants, but individualized monochromatic values should be used for different metals This significant noise burden at lower energies has been a limited factor in the actual application of virtual monochromatic imaging. Valenti [19] explored a new field of discrete tomography which directly reconstructed images in very few projections with instrumental and quantization noises These methods used in denoising only consider each energy bin separately and cannot effectively make full use of the correlation between different energy bins for more information. Small differences in attenuation can be diagnostically important, but the optimal energy for synthesizing a virtual monochromatic image depends on many uncertain model factors, including patients size, data acquisition schemes, and noise levels in low energy bins [10].

METHODS

NETWORK STRUCTRUE

NETWORK TRAINING PROCESS

DISCUSSION

CONCLUSION