咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >A Multiscale Reconstruction Fr... 收藏

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

作     者:Li, Yimin Zhao, Yuanyuan Ma, Chenyang Li, Fangzhi Wang, Ziyao Lv, Wenjuan Ji, Dongjiang Jian, Jianbo Zhao, Xinyan Zhao, Yuqing Hu, Chunhong 

作者机构:Tianjin Med Univ Sch Biomed Engn & Technol Tianjin 300070 Peoples R China Tianjin Univ Technol & Educ Sch Sci Tianjin 300222 Peoples R China Tianjin Med Univ Gen Hosp Tianjin 300052 Peoples R China Capital Med Univ Beijing Friendship Hosp Liver Res Ctr Beijing 100050 Peoples R China 

出 版 物:《IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT》 (IEEE Trans. Instrum. Meas.)

年 卷 期:2025年第74卷

核心收录:

学科分类:0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 

基  金:National Natural Science Foundation of China [82402261, 82371960, 82102037, 82071922] High-Performance Computing Platform of Tianjin Medical University 

主  题:Image reconstruction Image edge detection Computed tomography Imaging X-ray imaging TV Detectors Decoding Reconstruction algorithms Phase measurement Edge enhancement in-line X-ray phase-contrast computed tomography (IL-XPCCT) limited-angle reconstruction phase retrieval 

摘      要: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 conducted to evaluate the performance of our proposed MSRF-EEG framework. Compared with the existing competitive algorithms, both quantitative and qualitative results demonstrate that the proposed method can significantly enhance reconstruction quality under different sampling angles and improve detail restoration and edge preservation.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分