The PyHST2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
The PyHST2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
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DOI：
10.1016/j.nimb.2013.09.030
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发表时间：
2014-04-01
影响因子：
1.3
通讯作者：
Kieffer, Jerome
中科院分区：
文献类型：
--
作者：
Mirone, Alessandro;Brun, Emmanuel;Kieffer, Jerome
我们提出了 PyHST2 代码，该代码在 ESRF 中用于相衬和吸收断层扫描。该代码经过精心设计，通过采用分布式管道架构来维持第三代同步加速器设施典型的高数据流（每个实验 10 TB）。除了默认的滤波反投影重建之外，该代码还实现了具有先验知识的迭代重建技术。后者用于提高重建质量或减少所需的数据量或样品的沉积剂量并达到给定的质量目标。所实现的先验知识技术基于总变异惩罚和最近发现的基于重叠补丁的凸函数。我们详细介绍了不同的方法，并讨论了如何在 PyHST2 代码中实现它们，该代码是在免费许可下分发的。我们提供了在缺乏真实数据的情况下估计先验技术的最佳参数值的方法。 (C) 2014 Elsevier B.V. 保留所有权利。
We present the PyHST2 code which is in service at ESRF for phase-contrast and absorption tomography. This code has been engineered to sustain the high data flow typical of the 3rd generation synchrotron facilities (10 terabytes per experiment) by adopting a distributed and pipelined architecture. The code implements, beside a default filtered backprojection reconstruction, iterative reconstruction techniques with a priori knowledge. These latter are used to improve the reconstruction quality or in order to reduce the required data volume or the deposited dose to the sample and reach a given quality goal. The implemented a priori knowledge techniques are based on the total variation penalization and a new recently found convex functional which is based on overlapping patches. We give details of the different methods and discuss how they are implemented in the PyHST2 code, which is distributed under free license. We provide methods for estimating, in the absence of ground-truth data, the optimal parameters values for a priori techniques. (C) 2014 Elsevier B.V. All rights reserved.