BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

X-ray diffraction based microscopy techniques such as high energy diffraction microscopy rely on knowledge of position of diffraction peaks with high resolution. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback for experiments. To this end, this paper proposes BraggNN, a machine learning-based method that can localize Bragg peak much more rapidly than conventional pseudo-Voigt peak fitting. When applied to our test dataset, BraggNN gives errors of less than 0.29 and 0.57 voxels, relative to conventional method, for 75% and 95% of the peaks, respectively. When applied to a real experiment dataset, a 3D reconstruction using peak positions located by BraggNN yields an average grain position difference of 17 micrometer and size difference of 1.3 micrometer as compared to the results obtained when the reconstruction used peaks from conventional 2D pseudo-Voigt fitting. Recent advances in deep learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster than a conventional method when using a GPU card with out-of-the-box software.