X-ray diffraction based microscopy techniques such as High Energy Diffraction Microscopy rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in area 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 detection and shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback during in-situ experiments. To this end, we propose BraggNN, a deep learning-based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, BraggNN gives errors of less than 0.29 and 0.57 pixels, relative to the conventional method, for 75% and 95% of the peaks, respectively. When applied to a real experimental dataset, a 3D reconstruction that used peak positions computed by BraggNN yields 15% better results on average as compared to a reconstruction obtained using peak positions determined using 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 on a consumer-class GPU card with out-of-the-box software.
翻译:X射线分解法基于显微镜的显微镜技术,如高能分解法显微镜,依靠对折叠峰位置的了解,且精密度高。这些位置通常通过将区域检测数据的观察到强度与伪Voigt等理论峰值形状相匹配来计算。随着实验变得更加复杂,检测技术也不断演化,这种峰值检测和形状安装的计算成本成为现场实验中实时反馈所需的快速分析的最大障碍。为此,我们提议BraggNNN,这是一种深层次的基于学习的方法,可以比传统的伪Voigt假显像峰值安装更迅速地确定峰值位置。当应用测试数据集时,BraggNNN提供比常规75%和95%的峰值形状形状形状更小0.29和0.57个像素差。当应用真正的实验数据集时,使用BraggNNN的峰值位置进行3D的重建,平均结果为15%,而利用使用常规的2D伪Voigt峰值模型确定峰值位置进行的重建,比传统的伪伏特峰值峰值峰值峰值峰值设置要快得多。最近BggNNNNNNN在常规标准方法上,在使用传统的硬值方法上取得比传统的硬值方法进行快速的升级的进度比BBRPF-ro-ro-cfor-ro-cx法的进度的进度的进度比B-c-c-la-c-c-la-c-la-la-la-c-c-la-la-la-la-la-la-laxx的进度法的升级法的进度要快。