Dual-domain network with transfer learning for bowtie-filter induced artifacts in half-fan cone-beam CT

Abstract

In a cone-beam CT system, the use of bowtie-filter may induce artifacts in the reconstructed images. Through a Monte-Carlo simulation study, we confirm that the bowtie filter causes spatially biased beam energy difference thereby creating beam-hardening artifacts. We also note that cupping artifacts in conjunction with the object scatter and additional beam-hardening may manifest. In this study, we propose a dual-domain network for reducing the bowtie-filter induced artifacts by addressing the origin of artifacts. In the projection domain, the network compensates for the filter induced beam-hardening effects. In the image domain, the network reduces the cupping artifacts that generally appear in cone-beam CT images. Also, transfer learning scheme was adopted in the projection domain network to reduce the total training costs and to increase utility in the practical cases while maintaining the robustness of the dual-domain network. Thus, the pre-trained projection domain network using simple elliptical cylinder phantoms was utilized. As a result, the proposed network shows denoised and enhanced soft-tissue contrast images with much reduced image artifacts. For comparison, a single image domain U-net was also implemented as an ablation study. The proposed dual-domain network outperforms, in terms of soft-tissue contrast and residual artifacts, a single domain network that does not physically consider the cause of artifacts.