Towards tender X-rays with Zernike phase-contrast imaging of biological samples at 50 nm resolution

Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.

Illuminating the Brain With X-Rays: Contributions and Future Perspectives of High-Resolution Microtomography to Neuroscience

The assessment of three-dimensional (3D) brain cytoarchitecture at a cellular resolution remains a great challenge in the field of neuroscience and constant development of imaging techniques has become crucial, particularly when it comes to offering direct and clear obtention of data from macro to nano scales. Magnetic resonance imaging (MRI) and electron or optical microscopy, although valuable, still face some issues such as the lack of contrast and extensive sample preparation protocols. In this context, x-ray microtomography (μCT) has become a promising non-destructive tool for imaging a broad range of samples, from dense materials to soft biological specimens. It is a new supplemental method to be explored for deciphering the cytoarchitecture and connectivity of the brain. This review aims to bring together published works using x-ray μCT in neurobiology in order to discuss the achievements made so far and the future of this technique for neuroscience.

Hard X-ray nano-holotomography with a Fresnel zone plate

X-ray phase contrast nanotomography enables imaging of a wide range of samples with high spatial resolution in 3D. Near-field holography, as one of the major phase contrast techniques, is often implemented using X-ray optics such as Kirkpatrick-Baez mirrors, waveguides and compound refractive lenses. However, these optics are often tailor-made for a specific beamline and challenging to implement and align. Here, we present a near-field holography setup based on Fresnel zone plates which is fast and easy to align and provides a smooth illumination and flat field. The imaging quality of different types of Fresnel zone plates is compared in terms of the flat-field quality, the achievable resolution and exposure efficiency i.e. the photons arriving at the detector. Overall, this setup is capable of imaging different types of samples at high spatial resolution of below 100 nm in 3D with access to the quantitative phase information.

A comparison of absorption and phase contrast for X-ray imaging of biological cells

X-ray imaging allows biological cells to be examined at a higher resolution than possible with visible light and without some of the preparation difficulties associated with electron microscopy of thick samples. The most used and developed technique is absorption contrast imaging in the water window which exploits the contrast between carbon and oxygen at an energy of around 500 eV. A variety of phase contrast techniques are also being developed. In general these operate at a higher energy, enabling thicker cells to be examined and, in some cases, can be combined with X-ray fluorescence imaging to locate specific metals. The various methods are based on the differences between the complex refractive indices of the cellular components and the surrounding cytosol or nucleosol, the fluids present in the cellular cytoplasm and nucleus. The refractive indices can be calculated from the atomic composition and density of the components. These in turn can be obtained from published measurements using techniques such as chemical analysis, scanning electron microscopy and X-ray imaging at selected energies. As examples, the refractive indices of heterochromatin, inner mitochondrial membranes, the neutral core of lipid droplets, starch granules, cytosol and nucleosol are calculated. The refractive index calculations enable the required doses and fluences to be obtained to provide images with sufficient statistical significance, for X-ray energies between 200 and 4000 eV. The statistical significance (e.g. the Rose criterion) for various requirements is discussed. The calculations reveal why some cellular components are more visible by absorption contrast and why much greater exposure times are required to see some cellular components. A comparison of phase contrast as a function of photon energy with absorption contrast in the water window is provided and it is shown that much higher doses are generally required for the phase contrast measurements. This particularly applies to those components with a high carbon content but with a mass density similar to the surrounding cytosol or nucleosol. The results provide guidance for the most appropriate conditions for X-ray imaging of individual cellular components within cells of various thicknesses.

Quantitative phase retrieval in X-ray Zernike phase contrast microscopy

In recent years, increasing attention has been devoted to X-ray phase contrast imaging, since it can provide high-contrast images by using phase variations. Among the different existing techniques, Zernike phase contrast microscopy is one of the most popular phase-sensitive techniques for investigating the fine structure of the sample at high spatial resolution. In X-ray Zernike phase contrast microscopy, the image contrast is indeed a mixture of absorption and phase contrast. Therefore, this technique just provides qualitative information on the object, which makes the interpretation of the image difficult. In this contribution, an approach is proposed for quantitative phase retrieval in X-ray Zernike phase contrast microscopy. By shifting the phase of the direct light by π/2 and 3π/2, two images of the same object are measured successively. The phase information of the object can then be quantitatively retrieved by a proper combination of the measured images. Numerical experiments were carried out and the results confirmed the feasibility of the proposed method. It is expected that the proposed method will find widespread applications in biology, materials science and so on.