VBVT-Net: VOI-Based VVBP-Tensor Network for High-Attenuation Artifact Suppression in Digital Breast Tomosynthesis Imaging

Abstract

High-attenuation (HA) artifacts may lead to obscured subtle lesions and lesion over-estimation in digital breast tomosynthesis (DBT) imaging. High-attenuation artifact suppression (HAAS) is vital for widespread DBT applications in clinic. The conventional HAAS methods usually rely on the segmentation accuracy of HA objects and manual weighting schemes, without considering the geometry information in DBT reconstruction. And the global weighted strategy designed for HA artifacts may decrease the resolution in low-contrast soft-tissue regions. Moreover, the view-by-view backprojection tensor (VVBP-Tensor) domain has recently developed as a new intermediary domain that contains the lossless information in projection domain and the structural details in image domain. Therefore, we propose a VOI-Based VVBP-Tensor Network (VBVT-Net) for HAAS task in DBT imaging, which learns a local implicit weighted strategy based on the analytical FDK reconstruction mechanism. Specifically, the VBVT-Net method incorporates a volume of interest (VOI) recognition sub-network and a HAAS sub-network. The VOI recognition sub-network automatically extracts all 4D VVBP-Tensor patches containing HA artifacts. The HAAS sub-network reduces HA artifacts in these 4D VVBP-Tensor patches by leveraging the ray-trace backprojection features and extra neighborhood information. All results on four datasets demonstrate that the proposed VBVT-Net method could accurately detect HA regions, effectively reduce HA artifacts and simultaneously preserve structures in soft-tissue background regions. The proposed VBVT-Net method has a good interpretability as a general variant of the weighted FDK algorithm, which is potential to be applied in the next generation DBT prototype system in the future.