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Locally linear transform based three-dimensional gradient L 0 -norm minimization for spectral CT reconstruction.
Medical Physics ( IF 3.8 ) Pub Date : 2020-08-02 , DOI: 10.1002/mp.14420
Qian Wang 1 , Weiwen Wu 1, 2 , Shiwo Deng 3 , Yining Zhu 3 , Hengyong Yu 1
Affiliation  

Spectral computed tomography (CT) is proposed by extending the conventional CT along the energy dimension. One newly implementation is to employ an energy‐discriminating photon counting detector (PCD), which can distinguish photon energy and divide a whole x‐ray spectrum into several energy bins with appropriate post‐processing steps. The state‐of‐the‐art PCD‐based spectral CT has superior energy resolution and material distinguishability, and it further has a great potential in both medical and industrial applications. To improve the reconstruction quality and decomposition accuracy, in this work, we propose an optimization‐based spectral CT reconstruction method with an innovational sparsity constraint.

中文翻译:

基于局部线性变换的三维梯度L 0范数最小化,用于频谱CT重建。

通过沿能量维度扩展常规CT,提出了光谱计算机断层扫描(CT)。一种新的实现方式是采用能量区分光子计数检测器(PCD),该检测器可以区分光子能量,并通过适当的后处理步骤将整个X射线光谱分成几个能量仓。基于PCD的最新光谱CT具有出色的能量分辨率和材料可分辨性,并且在医学和工业应用中都具有巨大的潜力。为了提高重建质量和分解精度,在这项工作中,我们提出了一种具有创新性稀疏约束的基于优化的频谱CT重建方法。
更新日期:2020-08-02
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