Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization.
Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization.
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DOI：
10.1088/0031-9155/60/5/1741
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发表时间：
2015-03-07
影响因子：
3.5
通讯作者：
La Rivière PJ
中科院分区：
文献类型：
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作者：
Rigie DS;La Rivière PJ
我们探讨使用最近提出的“总核变差”（TVN）作为正则化重建多通道，光谱CT图像。这种凸罚函数是全变分（TV）到矢量值图像的自然扩展，具有鼓励图像通道之间的公共边缘位置和共享梯度方向的优点。我们展示了它如何可以被纳入一个一般的，数据约束的重建框架，并推导出更新方程的基础上的一阶，原始对偶算法的Chambolle和Pock。基于数值XCAT体模的早期模拟研究表明，与独立的逐通道TV重建相比，TVN引入的通道间耦合导致在高水平正则化下更好地保留图像特征。
We explore the use of the recently proposed “total nuclear variation” (TVN) as a regularizer for reconstructing multi-channel, spectral CT images. This convex penalty is a natural extension of the total variation (TV) to vector-valued images and has the advantage of encouraging common edge locations and a shared gradient direction among image channels. We show how it can be incorporated into a general, data-constrained reconstruction framework and derive update equations based on the first-order, primal-dual algorithm of Chambolle and Pock. Early simulation studies based on the numerical XCAT phantom indicate that the inter-channel coupling introduced by the TVN leads to better preservation of image features at high levels of regularization, compared to independent, channel-by-channel TV reconstructions.