A Helical Reconstruction Network for Multi-Source Static CT

Abstract

Nanovision static CT is an innovative CT scanning technique that features the arrangement of the X-ray source array and detector array on two parallel planes with a consistent offset. This configuration significantly enhances temporal resolution compared to conventional CT, providing particular advantages for dynamic organ imaging and low-dose imaging applications. However, it also introduces cone angle and sparse angle artifacts during helical scanning. To address this, this paper proposes a novel theoretical analysis framework to systematically analyze the artifact generation mechanism of the traditional FDK algorithm in this scenario. Through numerical solutions and data superposition, we are able to attribute the causes of artifacts for the first time to two types of data incompleteness issues arising from the lack of cone angle data and insufficient sparse angular sampling. Building on these insights, we propose an innovative dual-module collaborative reconstruction network. First, we introduce the Helical Bi-directional xFDK algorithm (HbixFDK), which employs a limited-angle weighted compensation strategy to mitigate data incompleteness in the cone angle region. Next, we develop the attention-based Helical FISTA network (HFISTA-Net), which utilizes the output from HbixFDK as the initial reconstruction to effectively suppress sparse sampling artifacts. Extensive experiments conducted on the TCIA dataset and clinical static CT scans demonstrate that our proposed method significantly reduces both cone angle and sparse angle artifacts in static CT helical scanning. The approach achieves rapid and high-precision helical reconstruction, showcasing superior accuracy and computational efficiency.