标题： 基于物理信息的超稀疏时空数据的4D X射线图像重建 显示英文标题

标题： Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data

摘要： 现代 X 射线源提供的前所未有的 X 射线通量密度为快速动态过程的 X 射线成像提供了新的时空可能性。 利用这些可能性的方法通常会导致要么由于扫描速度有限而投影数量或空间信息有限（如时间分辨断层扫描中那样），要么时间点的数量有限（如频闪成像中那样），从而使重建问题变得不适定，并且不太可能通过经典的重建方法解决。 从这类数据进行 4D 重建需要样本先验知识，这可以通过深度学习 (DL) 包括进来。 最先进的 X 射线成像 4D 重建方法结合了人工智能的力量和 X 射线传播的物理特性，以应对稀疏视图的挑战。 然而，大多数方法并没有约束所研究过程的物理特性，即完整的物理模型。 在这里，我们提出了 4D 物理信息优化神经隐式 X 射线成像 (4D-PIONIX)，这是一种新颖的物理信息 4D X 射线图像重建方法，结合了完整的物理模型和最先进的基于 DL 的 4D X 射线成像稀疏视图重建方法。 我们通过从模拟二元液滴碰撞的超稀疏时空获取中检索 4D 信息来展示并评估我们的方法的潜力，这是一个相关的流体力学过程。 我们预计这项工作将为各种 4D X 射线成像模式打开新的时空可能性，如时间分辨 X 射线断层扫描和更先进的稀疏采集方法（如 X 射线多投影成像），这将为各种快速 4D 动力学的研究铺平道路，如流体力学和复合材料测试。 显示英文摘要

摘要： The unprecedented X-ray flux density provided by modern X-ray sources offers new spatiotemporal possibilities for X-ray imaging of fast dynamic processes. Approaches to exploit such possibilities often result in either i) a limited number of projections or spatial information due to limited scanning speed, as in time-resolved tomography, or ii) a limited number of time points, as in stroboscopic imaging, making the reconstruction problem ill-posed and unlikely to be solved by classical reconstruction approaches. 4D reconstruction from such data requires sample priors, which can be included via deep learning (DL). State-of-the-art 4D reconstruction methods for X-ray imaging combine the power of AI and the physics of X-ray propagation to tackle the challenge of sparse views. However, most approaches do not constrain the physics of the studied process, i.e., a full physical model. Here we present 4D physics-informed optimized neural implicit X-ray imaging (4D-PIONIX), a novel physics-informed 4D X-ray image reconstruction method combining the full physical model and a state-of-the-art DL-based reconstruction method for 4D X-ray imaging from sparse views. We demonstrate and evaluate the potential of our approach by retrieving 4D information from ultra-sparse spatiotemporal acquisitions of simulated binary droplet collisions, a relevant fluid dynamic process. We envision that this work will open new spatiotemporal possibilities for various 4D X-ray imaging modalities, such as time-resolved X-ray tomography and more novel sparse acquisition approaches like X-ray multi-projection imaging, which will pave the way for investigations of various rapid 4D dynamics, such as fluid dynamics and composite testing.