| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
|
| Presentation Title |
Deep Convolutional Networks for Image Reconstruction from 3D Coherent X-ray Diffraction Imaging Data |
| Author(s) |
Mathew J. Cherukara, Henry Chan, Subramanian Sankaranarayanan, Youssef Nashed, Ross Harder |
| On-Site Speaker (Planned) |
Mathew J. Cherukara |
| Abstract Scope |
Coherent X-ray diffraction imaging (CDI) is a powerful technique for operando characterization. Visualizing defects, dynamics, and structural evolution using CDI, however, remains a grand challenge since state-of-the-art iterative reconstruction algorithms for CDI data are time-consuming and computationally expensive, which precludes real-time feedback. Furthermore, the reconstruction algorithms require human inputs to guide their convergence, which is a very subjective process. I will describe our work in the use of deep convolutional networks (CDI NN) in accelerating the analysis of, and potentially increasing the robustness of image recovery from 3D X-ray diffraction data. Once trained, CDI NN is hundreds of times faster than traditional phase retrieval algorithms used for image reconstruction from coherent diffraction data, opening up the prospect of real-time 3D imaging at the nanoscale. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |