42 research outputs found
Real-time sparse-sampled Ptychographic imaging through deep neural networks
Ptychography has rapidly grown in the fields of X-ray and electron imaging
for its unprecedented ability to achieve nano or atomic scale resolution while
simultaneously retrieving chemical or magnetic information from a sample. A
ptychographic reconstruction is achieved by means of solving a complex inverse
problem that imposes constraints both on the acquisition and on the analysis of
the data, which typically precludes real-time imaging due to computational cost
involved in solving this inverse problem. In this work we propose PtychoNN, a
novel approach to solve the ptychography reconstruction problem based on deep
convolutional neural networks. We demonstrate how the proposed method can be
used to predict real-space structure and phase at each scan point solely from
the corresponding far-field diffraction data. The presented results demonstrate
how PtychoNN can effectively be used on experimental data, being able to
generate high quality reconstructions of a sample up to hundreds of times
faster than state-of-the-art ptychography reconstruction solutions once
trained. By surpassing the typical constraints of iterative model-based
methods, we can significantly relax the data acquisition sampling conditions
and produce equally satisfactory reconstructions. Besides drastically
accelerating acquisition and analysis, this capability can enable new imaging
scenarios that were not possible before, in cases of dose sensitive, dynamic
and extremely voluminous samples
Multislice forward modeling of Coherent Surface Scattering Imaging on surface and interfacial structures
To study nanostructures on substrates, surface-sensitive reflection-geometry
scattering techniques such as grazing incident small angle x-ray scattering are
commonly used to yield an averaged statistical structural information of the
surface sample. Grazing incidence geometry can probe the absolute
three-dimensional structural morphology of the sample if a highly coherent beam
is used. Coherent Surface Scattering Imaging (CSSI) is a powerful yet
non-invasive technique similar to Coherent X-ray Diffractive Imaging (CDI) but
performed at small angles and grazing-incidence reflection geometry. A
challenge with CSSI is that conventional CDI reconstruction techniques cannot
be directly applied to CSSI because the Fourier-transform-based forward models
cannot reproduce the dynamical scattering phenomenon near the critical angle of
total external reflection of the substrate-supported samples. To overcome this
challenge, we have developed a multislice forward model which can successfully
simulate the dynamical or multi-beam scattering generated from surface
structures and the underlying substrate. The forward model is also demonstrated
to be able to reconstruct an elongated 3D pattern from a single shot scattering
image in the CSSI geometry through fast-performing CUDA-assisted PyTorch
optimization with automatic differentiation.Comment: 12 pages, 4 figures, 1 tabl
AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources
We present an end-to-end automated workflow that uses large-scale remote
compute resources and an embedded GPU platform at the edge to enable
AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
Ptychography is a lensless method that is being used to image samples through a
simultaneous numerical inversion of a large number of diffraction patterns from
adjacent overlapping scan positions. This acquisition method can enable
nanoscale imaging with x-rays and electrons, but this often requires very large
experimental datasets and commensurately high turnaround times, which can limit
experimental capabilities such as real-time experimental steering and
low-latency monitoring. In this work, we introduce a software system that can
automate ptychography data analysis tasks. We accelerate the data analysis
pipeline by using a modified version of PtychoNN -- an ML-based approach to
solve phase retrieval problem that shows two orders of magnitude speedup
compared to traditional iterative methods. Further, our system coordinates and
overlaps different data analysis tasks to minimize synchronization overhead
between different stages of the workflow. We evaluate our workflow system with
real-world experimental workloads from the 26ID beamline at Advanced Photon
Source and ThetaGPU cluster at Argonne Leadership Computing Resources.Comment: 7 pages, 1 figure, to be published in High Performance Computing for
Imaging Conference, Electronic Imaging (HPCI 2023
Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy
Understanding and interpreting dynamics of functional materials \textit{in
situ} is a grand challenge in physics and materials science due to the
difficulty of experimentally probing materials at varied length and time
scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited
for characterizing materials dynamics over wide-ranging time scales, however
spatial and temporal heterogeneity in material behavior can make interpretation
of experimental XPCS data difficult. In this work we have developed an
unsupervised deep learning (DL) framework for automated classification and
interpretation of relaxation dynamics from experimental data without requiring
any prior physical knowledge of the system behavior. We demonstrate how this
method can be used to rapidly explore large datasets to identify samples of
interest, and we apply this approach to directly correlate bulk properties of a
model system to microscopic dynamics. Importantly, this DL framework is
material and process agnostic, marking a concrete step towards autonomous
materials discovery
Deep learning at the edge enables real-time streaming ptychographic imaging
Coherent microscopy techniques provide an unparalleled multi-scale view of
materials across scientific and technological fields, from structural materials
to quantum devices, from integrated circuits to biological cells. Driven by the
construction of brighter sources and high-rate detectors, coherent X-ray
microscopy methods like ptychography are poised to revolutionize nanoscale
materials characterization. However, associated significant increases in data
and compute needs mean that conventional approaches no longer suffice for
recovering sample images in real-time from high-speed coherent imaging
experiments. Here, we demonstrate a workflow that leverages artificial
intelligence at the edge and high-performance computing to enable real-time
inversion on X-ray ptychography data streamed directly from a detector at up to
2 kHz. The proposed AI-enabled workflow eliminates the sampling constraints
imposed by traditional ptychography, allowing low dose imaging using orders of
magnitude less data than required by traditional methods