Gradient-based sparse approximation for computed tomography
Gradient-based sparse approximation for computed tomography
复制标题
基于梯度的稀疏近似计算机断层扫描
DOI：
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发表时间：
2015
期刊：
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通讯作者：
A. Entezari
中科院分区：
文献类型：
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作者：
Elham Sakhaee;M. Arreola;A. Entezari
有限数据的计算机断层扫描（CT）提出了图像重建算法的挑战，并已成为旨在减少X射线辐射暴露的研究的活跃主题。我们提出了一种新的配方的基础上稀疏近似的投影数据的图像梯度的断层重建。我们的方法利用偏导数的相互依赖性对优化问题施加额外的无卷曲约束。然后使用泊松求解器重建图像。实验结果表明，相比于全变分方法，我们的新配方提高了重建的精度显着在少数视图设置。
Limited-data Computed Tomography (CT) presents challenges for image reconstruction algorithms and has been an active topic of research aiming at reducing the exposure to X-ray radiation. We present a novel formulation for tomo-graphic reconstruction based on sparse approximation of the image gradients from projection data. Our approach leverages the interdependence of the partial derivatives to impose an additional curl-free constraint on the optimization problem. The image is then reconstructed using a Poisson solver. The experimental results show that, compared to total variation methods, our new formulation improves the accuracy of reconstruction significantly in few-view settings.