基于物理信息的超稀疏时空数据4D X射线图像重建
Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data
AI总结：
本文提出4D-PIONIX方法，结合完整物理模型与深度学习，从超稀疏时空数据中重建4D X射线图像，解决稀疏视图下重建问题，为快速动态过程研究提供新可能。
摘要：
现代X射线源提供的空前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：i) 由于扫描速度有限而导致的投影数量或空间信息有限，如时间分辨断层扫描；或 ii) 时间点数量有限，如频闪成像，这使得重建问题不适定，不太可能由经典重建方法解决。从此类数据进行4D重建需要样本先验，这可以通过深度学习（DL）纳入。X射线成像领域最先进的4D重建方法结合了AI的能力和X射线传播的物理特性来应对稀疏视图的挑战。然而，大多数方法并未约束所研究过程的物理特性，即完整的物理模型。在此，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的稀疏视图4D X射线成像重建方法。我们通过从模拟二元液滴碰撞（一种相关的流体动力学过程）的超稀疏时空采集数据中提取4D信息，展示并评估了我们方法的潜力。我们设想这项工作将为各种4D X射线成像模态开辟新的时空可能性，如时间分辨X射线断层扫描以及像X射线多投影成像等更新颖的稀疏采集方法，这将为各种快速4D动力学（如流体动力学和复合材料测试）的研究铺平道路。
英文摘要：
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.
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