Xu Qiaofeng, Sidky Emil Y, Pan Xiaochuan, Stampanoni Marco, Modregger Peter, Anastasio Mark A
Department of Biomedical Engineering, Washington University in St. Louis, Saint Louis, MO 63103, USA.
Opt Express. 2012 May 7;20(10):10724-49. doi: 10.1364/OE.20.010724.
Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.
差分X射线相衬断层扫描（DPCT）是一类很有前景的方法，用于重建对X射线吸收对比度较弱的材料的X射线折射率分布。DPCT中的断层投影数据（据此重建折射率分布估计值）对应于折射率分布二维（2D）拉东变换的一维（1D）导数。迫切需要开发用于DPCT的迭代图像重建方法，该方法能够从少视图投影数据中生成有用图像，从而减少与使用解析重建方法相关的长数据采集时间和大辐射剂量。在这项工作中，我们分析了构成DPCT迭代图像重建基础的两类离散成像模型的数值和统计特性。我们还研究了其中一个模型与现代图像重建算法一起用于对组织样本进行少视图图像重建的情况。
