Single-shot x-ray imaging of short-lived nanostructures such as clusters and nanoparticles near a phase transition or non-crystalizing objects such as large proteins and viruses is currently the most elegant method for characterizing their structure. Using hard x-ray radiation provides scattering images that encode two-dimensional projections, which can be combined to identify the full three-dimensional object structure from multiple identical samples. Wide-angle scattering using XUV or soft x-rays, despite yielding lower resolution, provides three-dimensional structural information in a single shot and has opened routes towards the characterization of non-reproducible objects in the gas phase. The retrieval of the structural information contained in wide-angle scattering images is highly non-trivial, and currently no efficient rigorous algorithm is known. Here we show that deep learning networks, trained with simulated scattering data, allow for fast and accurate reconstruction...

中文翻译：

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通过深度学习快速重建单张广角衍射图像

目前，对短寿命纳米结构（例如簇和相变附近的纳米颗粒）或非结晶物体（例如大蛋白和病毒）进行单次X射线成像是表征其结构的最优雅方法。使用硬X射线辐射可提供对二维投影进行编码的散射图像，可将其组合以从多个相同的样本中识别出完整的三维对象结构。尽管产生了较低的分辨率，但使用XUV或软X射线进行的广角散射可在单次发射中提供三维结构信息，并为表征气相中不可复制的物体开辟了道路。广角散射图像中包含的结构信息的检索非常重要，目前还没有有效的严格算法。在这里，我们表明，经过模拟散射数据训练的深度学习网络可实现快速准确的重建...