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Three-dimensional visualization of microvasculature from few-projection data using a novel CT reconstruction algorithm for propagation-based X-ray phase-contrast imaging

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单位: [1]School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China [2]The School of Science, Tianjin University of Technology and Education, Tianjin 300222, China [3]Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
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Propagation-based X-ray phase-contrast imaging (PBI) is a powerful nondestructive imaging technique that can reveal the internal detailed structures in weakly absorbing samples. Extending PBI to CT (PBCT) enables high-resolution and high-contrast 3D visualization of microvasculature, which can be used for the understanding, diagnosis and therapy of diseases involving vasculopathy, such as cardiovascular disease, stroke and tumor. However, the long scan time for PBCT impedes its wider use in biomedical and preclinical microvascular studies. To address this issue, a novel CT reconstruction algorithm for PBCT is presented that aims at shortening the scan time for microvascular samples by reducing the number of projections while maintaining the high quality of reconstructed images. The proposed algorithm combines the filtered backprojection method into the iterative reconstruction framework, and a weighted guided image filtering approach (WGIF) is utilized to optimize the intermediate reconstructed images. Notably, the homogeneity assumption on the microvasculature sample is adopted as prior knowledge, and therefore, a prior image of microvasculature structures can be acquired by a k-means clustering approach. Then, the prior image is used as the guided image in the WOLF procedure to effectively suppress streaking artifacts and preserve microvasculature structures. To evaluate the effectiveness and capability of the proposed algorithm, simulation experiments on 3D microvasculature numerical phantom and real experiments with CT reconstruction on the microvasculature sample are performed. The results demonstrate that the proposed algorithm can, under noise-free and noisy conditions, significantly reduce the artifacts and effectively preserve the microvasculature structures on the reconstructed images and thus enables it to be used for clear and accurate 3D visualization of microvasculature from few-projection data. Therefore, for 3D visualization of microvasculature, the proposed algorithm can be considered an effective approach for reducing the scan time required by PBCT. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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出版当年[2019]版:
大类 | 2 区 医学
小类 | 2 区 光学 2 区 核医学 3 区 生化研究方法
最新[2025]版:
大类 | 3 区 医学
小类 | 2 区 生化研究方法 3 区 光学 3 区 核医学
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出版当年[2018]版:
Q1 BIOCHEMICAL RESEARCH METHODS Q1 OPTICS Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q2 BIOCHEMICAL RESEARCH METHODS Q2 OPTICS Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均[2021-2025] 出版当年[2018版] 出版当年五年平均[2014-2018] 出版前一年[2017版] 出版后一年[2019版]

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第一作者单位: [1]School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
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