4D-ONIX：一种从稀疏X射线投影重建三维电影的深度学习方法
4D-ONIX: A deep learning approach for reconstructing 3D movies from sparse X-ray projections
AI总结：
4D-ONIX利用深度学习结合X射线物理模型，从每个时间戳仅2-3个稀疏投影重建高质量三维电影，在液滴碰撞和增材制造实验中验证了其泛化能力。
摘要：
X射线自由电子激光器和存储环提供的X射线通量为研究原位和工况动态提供了新的时空可能性，甚至可以使用这些设施的单脉冲。X射线多投影成像（XMPI）是一种新技术，能够利用此类设施的单脉冲获取体积信息，并避免由最先进的时间分辨三维方法（如时间分辨断层扫描）引起的离心力。因此，XMPI有潜力以比当前方法至少快三个数量级的速度获取三维电影（4D）。然而，使用当前算法从XMPI获取的高度稀疏投影中重建4D极具挑战性。在此，我们提出4D-ONIX，一种基于深度学习（DL）的方法，学习从极其有限数量的投影中重建三维电影（4D）。它结合了X射线与物质相互作用的计算物理模型和最先进的深度学习方法。我们展示了4D-ONIX在二元液滴碰撞和增材制造中，通过每个时间戳仅两到三个投影对多个实验进行泛化，生成高质量4D的潜力。我们设想4D-ONIX将成为4D分析的使能工具，提供新的时空分辨率来研究以前不可能研究的过程。
英文摘要：
The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI offers the potential to acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, it is exceptionally challenging to reconstruct 4D from highly sparse projections as acquired by XMPI with current algorithms. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies (4D) from an extremely limited number of projections. It combines the computational physical model of X-ray interaction with matter and state-of-the-art DL methods. We demonstrate the potential of 4D-ONIX to generate high-quality 4D by generalizing over multiple experiments with only two to three projections per timestamp for binary droplet collisions and additive manufacturing. We envision that 4D-ONIX will become an enabling tool for 4D analysis, offering new spatiotemporal resolutions to study processes not possible before.
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