Real-time 3D Coherent Diffraction Data Inversion Through Deep Learning

M. Cherukara, H. Chan, T. Zhou, Y. Nashed, S. Sankaranarayanan, M. Holt, R. Harder
Argonne National Lab,
United States

Keywords: deep learning, 3D imaging, coherent imaging


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 Bragg diffraction data and 2D Ptychography 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.