Fast algorithms for nonlinear and constrained phase retrieval in near-field X-ray holography based on Tikhonov regularization

X-ray phase-contrast tomography of cells manipulated with an optical stretcher

X-rays can penetrate deeply into biological cells and thus allow for examination of their internal structures with high spatial resolution. In this study, X-ray phase-contrast imaging and tomography is combined with an X-ray-compatible optical stretcher and microfluidic sample delivery. Using this setup, individual cells can be kept in suspension while they are examined with the X-ray beam at a synchrotron. From the recorded holograms, 2D phase shift images that are proportional to the projected local electron density of the investigated cell can be calculated. From the tomographic reconstruction of multiple such projections the 3D electron density can be obtained. The cells can thus be studied in a hydrated or even living state, thus avoiding artifacts from freezing, drying or embedding, and can in principle also be subjected to different sample environments or mechanical strains. This combination of techniques is applied to living as well as fixed and stained NIH3T3 mouse fibroblasts and the effect of the beam energy on the phase shifts is investigated. Furthermore, a 3D algebraic reconstruction scheme and a dedicated mathematical description is used to follow the motion of the trapped cells in the optical stretcher for multiple rotations.

ICF-PR-Net: a deep phase retrieval neural network for X-ray phase contrast imaging of inertial confinement fusion capsules

X-ray phase contrast imaging (XPCI) has demonstrated capability to characterize inertial confinement fusion (ICF) capsules, and phase retrieval can reconstruct phase information from intensity images. This study introduces ICF-PR-Net, a novel deep learning-based phase retrieval method for ICF-XPCI. We numerically constructed datasets based on ICF capsule shape features, and proposed an object-image loss function to add image formation physics to network training. ICF-PR-Net outperformed traditional methods as it exhibited satisfactory robustness against strong noise and nonuniform background and was well-suited for ICF-XPCI's constrained experimental conditions and single exposure limit. Numerical and experimental results showed that ICF-PR-Net accurately retrieved the phase and absorption while maintaining retrieval quality in different situations. Overall, the ICF-PR-Net enables the diagnosis of the inner interface and electron density of capsules to address ignition-preventing problems, such as hydrodynamic instability growth.

Artifact-suppressing reconstruction of strongly interacting objects in X-ray near-field holography without a spatial support constraint

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 of 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 operando measurements. In this paper, we will forego the spatial support constraint and propose a projected gradient descent (PGD) based reconstruction scheme in combination with proper preprocessing and regularization that significantly reduces artifacts for refractive reconstructions from only a single acquired hologram without a spatial support constraint. We demonstrate the feasibility and robustness of our approach on different data sets obtained at the nano imaging endstation of P05 at PETRA III (DESY, Hamburg) operated by Helmholtz-Zentrum Hereon.

Hierarchically guided in situ nanolaminography for the visualisation of damage nucleation in alloy sheets

Hierarchical guidance is developed for three-dimensional (3D) nanoscale X-ray imaging, enabling identification, refinement, and tracking of regions of interest (ROIs) within specimens considerably exceeding the field of view. This opens up new possibilities for in situ investigations. Experimentally, the approach takes advantage of rapid multiscale measurements based on magnified projection microscopy featuring continuous zoom capabilities. Immediate and continuous feedback on the subsequent experimental progress is enabled by suitable on-the-fly data processing. For this, by theoretical justification and experimental validation, so-called quasi-particle phase-retrieval is generalised to conical-beam conditions, being key for sufficiently fast computation without significant loss of imaging quality and resolution compared to common approaches for holographic microscopy. Exploiting 3D laminography, particularly suited for imaging of ROIs in laterally extended plate-like samples, the potential of hierarchical guidance is demonstrated by the in situ investigation of damage nucleation inside alloy sheets under engineering-relevant boundary conditions, providing novel insight into the nanoscale morphological development of void and particle clusters under mechanical load. Combined with digital volume correlation, we study deformation kinematics with unprecedented spatial resolution. Correlation of mesoscale (i.e. strain fields) and nanoscale (i.e. particle cracking) evolution opens new routes for the understanding of damage nucleation within sheet materials with application-relevant dimensions.

Projected refractive index framework for multi-wavelength phase retrieval

Multi-wavelength phase retrieval provides a competitive solution to lensless holographic imaging that features a low-cost, compact design and high data acquisition speed. However, the existence of phase wraps poses a unique challenge for iterative reconstruction, and the resulting algorithms often suffer from limited generalizability and increased computational complexity. Here, we propose a projected refractive index framework for multi-wavelength phase retrieval that directly recovers the amplitude and unwrapped phase of the object. General assumptions are linearized and integrated into the forward model. Based on an inverse problem formulation, physical constraints and sparsity priors are incorporated, which ensures imaging quality under noisy measurements. We experimentally demonstrate high-quality quantitative phase imaging on a lensless on-chip holographic imaging system using three color LEDs.