A Multiscale Reconstruction Framework Based on Edge-Enhanced Guidance for In-Line X-Ray Phase-Contrast CT With Limited-Angle Projections

作者：Yimin Li, Yuanyuan Zhao, Chenyang Ma, Fangzhi Li, Ziyao Wang, Wenjuan Lv, Dongjiang Ji, Jianbo Jian, Xinyan Zhao, Yuqing Zhao, Chunhong Hu · 发表于：IEEE Transactions on Instrumentation and Measurement · 年份：2024 · DOI：10.1109/tim.2024.3522391 · 被引用次数：4 · 研究领域：Advanced X-ray Imaging Techniques、Medical Imaging Techniques and Applications、Advanced X-ray and CT Imaging

The in-line X-ray phase-contrast computed tomography (IL-XPCCT) serves as an effective tool for studying organ function and pathologies. However, IL-XPCCT approaches often result in high radiation doses due to long scan times. To address this issue, a limited angel sampling strategy is frequently employed. However, limited-angle projection data can lead to severe divergence phenomena at sample edges or boundaries, significantly reducing image quality and affecting subsequent image analysis. In this work, we propose a novel multiscale reconstruction framework based on edge-enhanced guidance (MSRF-EEG) for IL-XPCCT limited-angle CT reconstruction. The MSRF-EEG framework consists of two primary subnetworks: an edge-enhanced subnetwork (EESN) and a multiscale reconstruction subnetwork (MSRSN). By fully considering the imaging characteristics of the IL-XPCCT, the EESN is designed to explore the edge enhancement characteristics from the IL-XPCCT images prior to phase retrieval, which is then utilized to compensate for edge distortions during the reconstruction process. The MSRSN performs image reconstruction with edge-enhanced guidance across multiple scales simultaneously. Furthermore, to better leverage the edge enhancement characteristics before phase retrieval for reconstructing the IL-XPCCT images from limited-angle projections, an edge aggregation (EAG) module and an edge attention (EAT) module are incorporated into the MSRSN. Simulations and real data experiments are conduct...