Non-convexly constrained image reconstruction from nonlinear tomographic X-ray measurements
Non-convexly constrained image reconstruction from nonlinear tomographic X-ray measurements
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DOI：
10.1098/rsta.2014.0393
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
T. Blumensath;R. Boardman
中科院分区：
文献类型：
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作者：
T. Blumensath;R. Boardman
在层析X射线测量中使用多色X射线源导致非线性X射线透射效应。由于这些非线性在断层摄影重建中通常不被考虑，因此会出现伪影，这在对具有广泛变化的X射线衰减特性的多种材料的对象进行成像时可能特别严重。在这些设置中，基于非线性X射线传输模型的重建算法变得有价值。我们在这里研究使用这样的模型，并开发算法，施加额外的非凸约束的重建。这使我们能够重建体积数据，即使在有限的测量是可用的。我们提出了一个非线性共轭梯度迭代硬阈值算法，并显示了有多少事先建模的假设，可以使用一系列的非凸约束。
The use of polychromatic X-ray sources in tomographic X-ray measurements leads to nonlinear X-ray transmission effects. As these nonlinearities are not normally taken into account in tomographic reconstruction, artefacts occur, which can be particularly severe when imaging objects with multiple materials of widely varying X-ray attenuation properties. In these settings, reconstruction algorithms based on a nonlinear X-ray transmission model become valuable. We here study the use of one such model and develop algorithms that impose additional non-convex constraints on the reconstruction. This allows us to reconstruct volumetric data even when limited measurements are available. We propose a nonlinear conjugate gradient iterative hard thresholding algorithm and show how many prior modelling assumptions can be imposed using a range of non-convex constraints.