Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography.
Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography.
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DOI：
10.1364/oe.18.010404
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发表时间：
2010-05-10
期刊：
影响因子：
3.8
通讯作者：
Pan X
中科院分区：
文献类型：
--
作者：
Sidky EY;Anastasio MA;Pan X
基于双折射的X射线相衬断层摄影（PCT）试图重建关于对象的复值折射率分布的信息。在许多应用中，寻求边界增强图像，其揭示折射率分布的实值分量中的不连续性的位置。我们研究了两种迭代算法，用于边界增强PCT中的少视图图像重建，该算法利用边界增强PCT图像或其梯度通常是稀疏的这一事实。为了利用对象的稀疏性，重建算法寻求最小化图像的T1范数或TV范数，受到数据一致性约束。我们证明，该算法可以重建准确的边界增强图像从高度不完整的少视图投影数据。
Propagation-based X-ray phase-contrast tomography (PCT) seeks to reconstruct information regarding the complex-valued refractive index distribution of an object. In many applications, a boundary-enhanced image is sought that reveals the locations of discontinuities in the real-valued component of the refractive index distribution. We investigate two iterative algorithms for few-view image reconstruction in boundary-enhanced PCT that exploit the fact that a boundary-enhanced PCT image, or its gradient, is often sparse. In order to exploit object sparseness, the reconstruction algorithms seek to minimize the ℓ1-norm or TV-norm of the image, subject to data consistency constraints. We demonstrate that the algorithms can reconstruct accurate boundary-enhanced images from highly incomplete few-view projection data.