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Published October 14, 2024 | Version 1.3.0
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A Python framework for the online reconstruction of X-ray near-field holography data

  • 1. ROR icon Hamburg University of Technology
  • 2. ROR icon Deutsches Elektronen-Synchrotron DESY
  • 3. ROR icon Helmholtz-Zentrum Hereon
  • 1. ROR icon Hamburg University of Technology
  • 2. ROR icon Deutsches Elektronen-Synchrotron DESY
  • 3. ROR icon University Medical Center Hamburg-Eppendorf
  • 4. ROR icon Helmholtz-Zentrum Hereon
  • 5. ROR icon Forschungszentrum Jülich
  • 6. ROR icon Universität Hamburg
  • 7. ROR icon University of Rostock
  • 8. ROR icon Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering

Description

The phase problem is a well known ill-posed reconstruction problem of coherent lens-less microscopic imaging, where only the squared magnitude of a complex wavefront is measured by a detector while the phase information from the wave field is lost. To retrieve the lost information, common algorithms rely either on multiple data acquisitions under varying measurement conditions or on the application of strong constraints such as a spatial support. In X-ray near-field holography however, these methods are rendered impractical in the setting of time sensitive in situ and in operando measurements. In this framework, we forego the spatial support constraint and propose a projected gradient descent (PGD) based reconstruction scheme in combination with proper preprocessing that significantly reduces artifacts for refractive reconstructions from only a single acquired hologram without a spatial support constraint. This repository contains the implementation and examples shown in the paper "Artifact-suppressing reconstruction of strongly interacting objects in X-ray near-field holography without a spatial support constraint". https://doi.org/10.1364/OE.514641

For the reconstruction of sharp images from experimental holograms in the near-field regime, it is crucial to precisely estimate the Fresnel number of the forward model. Otherwise, blurred out-of focus images that also can contain artifacts are the result. In general, a simple distance measurement at the experimental setup is not sufficiently accurate, thus the fine-tuning of the Fresnel number has to be done prior to the actual phase retrieval. This can be done manually or automatically by an estimation algorithm. In this framework, we propose a novel criterion, based on a model matching approach with respect to the underlying reconstruction of the projected refractive index of an object. With respect to this criterion, we provide a downhill-simplex method for the automatic optimization of the Fresnel number. This approach provides  a solutIon for automatic focusing for the phase retrieval of near-field holograms.

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Additional details

Related works

Is supplement to
Journal article: 10.1364/OE.514641 (DOI)

Funding

Helmholtz Association of German Research Centres
DASHH (Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter) HIDSS-0002
Deutsche Forschungsgemeinschaft
SFB 986 192346071
Helmholtz Association of German Research Centres
Helmholtz Imaging - SmartPhase ZT-I-PF-4-027