Synchrotron-based x ray fluorescence ghost imaging.

X ray fluorescence ghost imaging (XRF-GI) was recently demonstrated for x ray lab sources. It has the potential to reduce the acquisition time and deposited dose by choosing their trade-off with a spatial resolution while alleviating the focusing constraints of the probing beam. Here, we demonstrate the realization of synchrotron-based XRF-GI: we present both an adapted experimental setup and its corresponding required computational technique to process the data. This extends the above-mentioned potential advantages of GI to synchrotron XRF imaging. In addition, it enables new strategies to improve resilience against drifts at all scales and the study of previously inaccessible samples, such as liquids.

Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python.

Tomography is a powerful tool for reconstructing the interior of an object from a series of projection images. Typically, the source and detector traverse a standard path (e.g., circular, helical). Recently, various techniques have emerged that use more complex acquisition geometries. Current software packages require significant handwork, or lack the flexibility to handle such geometries. Therefore, software is needed that can concisely represent, visualize, and compute reconstructions of complex acquisition geometries. We present tomosipo, a Python package that provides these capabilities in a concise and intuitive way. Case studies demonstrate the power and flexibility of tomosipo.

Cryo-nanoimaging of Single Human Macrophage Cells: 3D Structural and Chemical Quantification.

X-ray microscopy is increasingly used in biology, but in most cases only in a qualitative way. We present here a 3D correlative cryo X-ray microscopy approach suited for the quantification of molar concentrations and structure in native samples at nanometer scale. The multimodal approach combines X-ray fluorescence and X-ray holographic nanotomography on "thick" frozen-hydrated cells. The quantitativeness of the X-ray fluorescence reconstruction is improved by estimating the self-attenuation from the 3D holography reconstruction. Applied to complex macrophage cells, we extract the quantification of major and minor elements heavier than phosphorus, as well as the density, in the different organelles. The intracellular landscape shows remarkable elemental differences. This novel analytical microscopy approach will be of particular interest to investigate complex biological and chemical systems in their native environment.

Advanced light-field refocusing through tomographic modeling of the photographed scene.

Recently we have shown that light-field photography images can be interpreted as limited-angle cone-beam tomography acquisitions. Here, we use this property to develop a direct-space tomographic refocusing formulation that allows one to refocus both unfocused and focused light-field images. We express the reconstruction as a convex optimization problem, thus enabling the use of various regularization terms to help suppress artifacts, and a wide class of existing advanced tomographic algorithms. This formulation also supports super-resolved reconstructions and the correction of the optical system's limited frequency response (point spread function). We validate this method with numerical and real-world examples.

Tomographic approach for the quantitative scene reconstruction from light field images.

Current computational methods for light field photography model the ray-tracing geometry inside the plenoptic camera. This representation of the problem, and some common approximations, can lead to errors in the estimation of object sizes and positions. We propose a representation that leads to the correct reconstruction of object sizes and distances to the camera, by showing that light field images can be interpreted as limited angle cone-beam tomography acquisitions. We then quantitatively analyze its impact on image refocusing, depth estimation and volumetric reconstructions, comparing it against other possible representations. Finally, we validate these results with numerical and real-world examples.

Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data.

Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality.

Emulation of X-ray Light-Field Cameras.

X-ray plenoptic cameras acquire multi-view X-ray transmission images in a single exposure (light-field). Their development is challenging: designs have appeared only recently, and they are still affected by important limitations. Concurrently, the lack of available real X-ray light-field data hinders dedicated algorithmic development. Here, we present a physical emulation setup for rapidly exploring the parameter space of both existing and conceptual camera designs. This will assist and accelerate the design of X-ray plenoptic imaging solutions, and provide a tool for generating unlimited real X-ray plenoptic data. We also demonstrate that X-ray light-fields allow for reconstructing sharp spatial structures in three-dimensions (3D) from single-shot data.

Numerical methods for low-dose EDS tomography.

Energy-dispersive X-ray spectroscopic (EDS) tomography is a powerful three-dimensional (3D) imaging technique for characterizing the chemical composition and structure of nanomaterials. However, the accuracy and resolution are typically hampered by the limited number of tilt images that can be measured and the low signal-to-noise ratios (SNRs) of the energy-resolved tilt images. Various sophisticated reconstruction algorithms have been proposed for specific types of samples and imaging conditions, yet deciding on which algorithm to use for each new case remains a complex problem. In this paper, we propose to tailor the reconstruction algorithm for EDS tomography in three aspects: (1) model the reconstruction problem based on an accurate assumption of the data statistics; (2) regularize the reconstruction to incorporate prior knowledge; (3) apply bimodal tomography to augment the EDS data with a high-SNR modality. Methods for the three aspects can be combined in one reconstruction procedure as three modules. Therefore, a reconstruction algorithm can be constructed as a 'recipe'. We also provide guidelines for preparing the recipe based on conditions and assumptions for the data. We investigate the effects of different recipes on both simulated data and real experimental data. The results show that the preferred recipe depends on both acquisition conditions and sample properties, and that the image quality can be enhanced using a properly tailored recipe.

Three-dimensional full-field X-ray orientation microscopy.

A previously introduced mathematical framework for full-field X-ray orientation microscopy is for the first time applied to experimental near-field diffraction data acquired from a polycrystalline sample. Grain by grain tomographic reconstructions using convex optimization and prior knowledge are carried out in a six-dimensional representation of position-orientation space, used for modelling the inverse problem of X-ray orientation imaging. From the 6D reconstruction output we derive 3D orientation maps, which are then assembled into a common sample volume. The obtained 3D orientation map is compared to an EBSD surface map and local misorientations, as well as remaining discrepancies in grain boundary positions are quantified. The new approach replaces the single orientation reconstruction scheme behind X-ray diffraction contrast tomography and extends the applicability of this diffraction imaging technique to material micro-structures exhibiting sub-grains and/or intra-granular orientation spreads of up to a few degrees. As demonstrated on textured sub-regions of the sample, the new framework can be extended to operate on experimental raw data, thereby bypassing the concept of orientation indexation based on diffraction spot peak positions. This new method enables fast, three-dimensional characterization with isotropic spatial resolution, suitable for time-lapse observations of grain microstructures evolving as a function of applied strain or temperature.