基于在线机器学习的大规模单粒子X射线衍射图像三维重建
Scalable 3D Reconstruction From Single Particle X-Ray Diffraction Images Based on Online Machine Learning
AI总结：
本文提出X-RAI在线重建框架，结合卷积编码器与基于物理的隐式神经表示解码器，实现大规模单粒子X射线衍射图像的高质量三维重建，支持百万级数据集处理。
摘要：
X射线自由电子激光器（XFELs）提供了测量生物分子结构和动力学的独特能力，有助于我们理解生命的基本构建模块。值得注意的是，高重复率XFELs使得单粒子成像（X射线SPI）成为可能，其中单个弱散射生物分子在接近生理条件下成像，有机会捕获在低温或结晶条件下无法捕捉的瞬态状态。现有的X射线SPI重建算法，用于估计每张捕获图像中粒子的未知取向及其共享的三维结构，不足以处理这些新兴XFELs产生的大规模数据集。在此，我们介绍了X-RAI，一种在线重建框架，从大型X射线SPI数据集中估计三维大分子的结构。X-RAI由一个卷积编码器组成，该编码器在大数据集上摊销姿态估计，以及一个基于物理的解码器，该解码器采用隐式神经表示，以端到端、自监督的方式实现高质量的三维重建。我们证明了X-RAI在模拟和具有挑战性的实验设置中的小规模数据集上实现了最先进的性能，并展示了其在线处理包含数百万衍射图像的大型数据集的空前能力。这些能力标志着X射线SPI向实时捕获和重建的范式转变。
英文摘要：
X-ray free-electron lasers (XFELs) offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. Notably, high-repetition-rate XFELs enable single particle imaging (X-ray SPI) where individual, weakly scattering biomolecules are imaged under near-physiological conditions with the opportunity to access fleeting states that cannot be captured in cryogenic or crystallized conditions. Existing X-ray SPI reconstruction algorithms, which estimate the unknown orientation of a particle in each captured image as well as its shared 3D structure, are inadequate in handling the massive datasets generated by these emerging XFELs. Here, we introduce X-RAI, an online reconstruction framework that estimates the structure of a 3D macromolecule from large X-ray SPI datasets. X-RAI consists of a convolutional encoder, which amortizes pose estimation over large datasets, as well as a physics-based decoder, which employs an implicit neural representation to enable high-quality 3D reconstruction in an end-to-end, self-supervised manner. We demonstrate that X-RAI achieves state-of-the-art performance for small-scale datasets in simulation and challenging experimental settings and demonstrate its unprecedented ability to process large datasets containing millions of diffraction images in an online fashion. These abilities signify a paradigm shift in X-ray SPI towards real-time capture and reconstruction.
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