14 research outputs found
Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction
Coherent diffraction imaging methods enable imaging beyond lens-imposed
resolution limits. In these methods, the object can be recovered by minimizing
an error metric that quantifies the difference between diffraction patterns as
observed, and those calculated from a present guess of the object. Efficient
minimization methods require analytical calculation of the derivatives of the
error metric, which is not always straightforward. This limits our ability to
explore variations of basic imaging approaches. In this paper, we propose to
substitute analytical derivative expressions with the automatic differentiation
method, whereby we can achieve object reconstruction by specifying only the
physics-based experimental forward model. We demonstrate the generality of the
proposed method through straightforward object reconstruction for a variety of
complex ptychographic experimental models.Comment: 23 pages (including references and supplemental material), 19
externally generated figure file
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
Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation
We describe and demonstrate an optimization-based x-ray image reconstruction
framework called Adorym. Our framework provides a generic forward model,
allowing one code framework to be used for a wide range of imaging methods
ranging from near-field holography to and fly-scan ptychographic tomography. By
using automatic differentiation for optimization, Adorym has the flexibility to
refine experimental parameters including probe positions, multiple hologram
alignment, and object tilts. It is written with strong support for parallel
processing, allowing large datasets to be processed on high-performance
computing systems. We demonstrate its use on several experimental datasets to
show improved image quality through parameter refinement
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
Efficient ptychographic phase retrieval via a matrix-free Levenberg-Marquardt algorithm
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