X-RAI
Scalable 3D Reconstruction for X-Ray Single Particle Imaging Based on Online Machine Learning
Frédéric Poitevin , Gordon Wetzstein , Jay Shenoy , Axel Levy
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.
References
Scalable 3D reconstruction for X-ray single particle imaging with online machine learning
Nature Communications (2025)
X-ray free-electron lasers offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. Notably, high-repetition-rate free-electron lasers enable single particle imaging, 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 single particle reconstruction algorithms, which estimate the particle orientation for each image independently, are slow and memory-intensive when handling the massive datasets generated by emerging free-electron lasers. Here, we introduce X-RAI (X-Ray single particle imaging with Amortized Inference), an online reconstruction framework that estimates the structure of 3D macromolecules from large X-ray single particle 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 single particle imaging towards real-time reconstruction.
Amortized pose estimation for x-ray single particle imaging
Machine learning for structural biology Workshop (2023)
X-ray single particle imaging (SPI) is a nascent technique that can capture the dynamics of biomolecules at room temperature. SPI experiments will one day collect tens of millions of images of the same molecule in order to overcome the weak scattering of individual proteins. Existing reconstruction algorithms will be unable to scale to datasets of this size because they perform computationally expensive search steps to estimate the orientation of the molecule in each image. In this work, we propose a reconstruction algorithm that amortizes the estimation of pose via an autoencoder framework. Our approach consists of a convolutional encoder that maps X-ray images to predicted poses and a physics-based decoder that implicitly fuses all the 2D scattering images into a volumetric representation of the molecule. We validate our method on 6 synthetic datasets of 2 distinct proteins, showing that for the largest datasets containing 5 million images, our technique can reconstruct the electron density in a single pass.