Low-dose x-ray tomography through a deep convolutional neural network.
Low-dose x-ray tomography through a deep convolutional neural network.
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DOI：
10.1038/s41598-018-19426-7
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发表时间：
2018-02-07
影响因子：
4.6
通讯作者：
Gürsoy D
中科院分区：
文献类型：
--
作者：
Yang X;De Andrade V;Scullin W;Dyer EL;Kasthuri N;De Carlo F;Gürsoy D
基于同步辐射的X射线断层扫描技术提供了以高分辨率快速大规模重建材料和生物组织内部的潜力。然而，对于辐射敏感的样品，在较长的采集时间期间损坏样品和在较短的采集时间内减少信号之间仍然存在基本的权衡。我们提出了一种深度卷积神经网络（CNN）方法，通过提高记录投影的质量，在低剂量快速采集期间将采集的X射线断层扫描信号增加至少10倍。用CNN增强的短曝光时间预测显示出与长曝光时间预测相似的信噪比。与其他技术后处理的低剂量短曝光采集相比，它们还显示出更低的噪声和更多的结构信息。我们评估了这种方法，使用模拟样品，并进一步验证了实验数据，从辐射敏感的小鼠大脑中获得的断层扫描设置与透射X射线显微镜。我们证明，自动算法可以可靠地跟踪用CNN增强的低剂量数据集中的大脑结构。该方法可应用于其他基于层析或扫描的X射线成像技术，并具有研究样品中更快动力学的巨大潜力
Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens
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DOI：
10.1016/j.elspec.2008.10.008
发表时间：
2009-03-01
影响因子：
1.9
作者：
Howells, M. R.;Beetz, T.;Chapman, H. N.;Cui, C.;Holton, J. M.;Jacobsen, C. J.;Kirz, J.;Lima, E.;Marchesini, S.;Miao, H.;Sayre, D.;Shapiro, D. A.;Spence, J. C. H.;Starodub, D.
通讯作者：
Starodub, D.
影响因子：
10.6
作者：
Han X;Bian J;Eaker DR;Kline TL;Sidky EY;Ritman EL;Pan X
通讯作者：
Pan X
影响因子：
2.6
作者：
HOUNDFIELD, GN
通讯作者：
HOUNDFIELD, GN
DOI：
10.1046/j.1365-2818.2000.00630.x
发表时间：
2000-01-01
期刊：
JOURNAL OF MICROSCOPY-OXFORD
影响因子：
--
作者：
Maser, J;Osanna, A;Tennant, D
通讯作者：
Tennant, D
影响因子：
3.8
作者：
Maier, Andreas;Wigstrom, Lars;Fahrig, Rebecca
通讯作者：
Fahrig, Rebecca