首页    期刊浏览 2023年02月08日 星期三
登录注册

文章基本信息

  • 标题:Low-dose x-ray tomography through a deep convolutional neural network
  • 本地全文:下载
  • 作者:Xiaogang Yang ; Vincent De Andrade ; William Scullin
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2018
  • 卷号:8
  • 期号:1
  • 页码:2575
  • DOI:10.1038/s41598-018-19426-7
  • 语种:English
  • 出版社:Springer Nature
  • 摘要: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.
国家哲学社会科学文献中心版权所有