Virtual non-metal network for metal artifact reduction in the sinogram domain

Abstract

Often, the artifacts caused by high-density objects degrade the quality of the image with streaks and data loss. In recent years, machine learning has proven itself a powerful tool to resolve some of the challenges faced in reducing metal artifacts. In this work, a novel method of metal artifact reduction (MAR) without metal segmentation by using a CNN network is proposed. The approach focuses on removing the need for the sensitive metal segmentation step to improve robustness and aims to tackle beam hardening directly in the sinogram domain. In the propose method, we trained the network with sinogram pairs that include metal objects and those that include virtual non-metal (VNM) replacement objects. A VNM object is designed to be less dense than metal but more dense than soft tissue. The novelty of this method lies in the sinogram-to-sinogram training without the need for metal segmentation by replacing the metal object to a virtual non-metal object in the sinogram to reduce beam hardening and successfully compensate for the data loss due to photon starvation.