Directional sinogram inpainting for limited angle tomography
Directional sinogram inpainting for limited angle tomography
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DOI：
10.1088/1361-6420/aaf2fe
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发表时间：
2018-04
期刊：
影响因子：
2.1
通讯作者：
R. Tovey;Martin Benning;C. Brune;M. J. Lagerwerf;S. Collins;R. Leary;P. Midgley;C. Schoenlieb
中科院分区：
文献类型：
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作者：
R. Tovey;Martin Benning;C. Brune;M. J. Lagerwerf;S. Collins;R. Leary;P. Midgley;C. Schoenlieb
在本文中，我们提出了一种新的联合模型重建有限角度采样制度下的层析成像数据。在断层摄影的许多应用中，例如电子显微镜和乳房X射线摄影，对采集的物理限制导致不能被采样的数据区域。根据限制的严重程度，重建可能包含严重的特征性伪影。我们的模型旨在通过在重建的同时修复丢失的数据来解决这些伪影。数值上，这个问题自然演变为需要最小化的非凸和非光滑的功能，所以我们回顾最近的工作在这个主题和扩展结果，以适应交替（块）下降框架。我们在两个合成数据集和一个电子显微镜数据集上进行了数值实验。我们的研究结果一致表明，联合修复和重建框架可以恢复更干净，更准确的结构信息比目前最先进的方法。
In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of tomography, e.g. electron microscopy and mammography, physical limitations on acquisition lead to regions of data which cannot be sampled. Depending on the severity of the restriction, reconstructions can contain severe, characteristic, artefacts. Our model aims to address these artefacts by inpainting the missing data simultaneously with the reconstruction. Numerically, this problem naturally evolves to require the minimisation of a non-convex and non-smooth functional so we review recent work in this topic and extend results to fit an alternating (block) descent framework. We perform numerical experiments on two synthetic datasets and one electron microscopy dataset. Our results show consistently that the joint inpainting and reconstruction framework can recover cleaner and more accurate structural information than the current state of the art methods.