Low-dose x-ray tomography through a deep convolutional neural network

• Authors: Xiaogang Yang, Argonne National Laboratory; Vincent De Andrade, Argonne National Laboratory; William Scullin, Argonne National Laboratory; Eva Dyer, Emory University; Narayanan Kasthuri, Argonne National Laboratory; Francesco De Carlo, Argonne National Laboratory; Doga Gürsoy, Argonne National Laboratory
• Journal Title: Scientific Reports
• Volume: Volume 8, Number 1
• Publisher: Nature Publishing Group: Open Access Journals - Option C | 2018-12-01, Pages 2575-2575
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1038/s41598-018-19426-7
• Subjects: Biology, Neuroscience; Engineering, Biomedical

Abstract

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.