Recent progress in synchrotron radiation 3D-4D nano-imaging based on X-ray full-field microscopy

Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.

A comparative study of EM-CCD and CMOS cameras for particle ion trajectory imaging

High-resolution and real-time imaging of particle ion trajectories is essential in nuclear medicine and nuclear engineering. One potential method to achieve high-resolution real-time trajectory imaging of particle ions involves utilizing an imaging system that integrates a scintillator plate with a magnifying unit and a cooled electron multiplying charge-coupled device (EM-CCD) camera. However, acquiring an EM-CCD camera might prove challenging due to the discontinuation of CCD sensor manufacturing by vendors. As an alternative imaging approach, a low-noise, high-sensitivity camera utilizing a cooled complementary metal-oxide-semiconductor (CMOS) sensor offers a promising solution for imaging particle ion trajectories. Yet, it remains uncertain whether CMOS-based cameras can perform as effectively as CCD-based cameras in capturing particle ion trajectories. To address these concerns, we conducted a comparative analysis of the imaging performance between a CMOS-based system and an EM-CCD-based system for capturing alpha particle trajectories. The results revealed that both systems could image the trajectories of alpha particle, but the spatial resolution with the CMOS-based camera exceeded that of the EM-CCD-based camera, primarily due to the smaller pixel size of the sensor. While the signal-to-noise ratio (SNR) of the trajectory image from the CMOS-based camera initially lagged behind that from the EM-CCD-based camera, this disparity was mitigated by implementing binning techniques on the CMOS-based camera images. In conclusion, our findings suggest that a cooled CMOS camera could serve as a viable alternative for imaging particle ion trajectories.

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.

Functional Output Regression for Machine Learning in Materials Science

In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we consider two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in material science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modeling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it effective in modeling even with small amounts of data. We demonstrate the proposed methods through several case studies in materials science.

In situ determination of the extreme damage resistance behavior in stomatopod dactyl club

The structure and mechanical properties of the stomatopod dactyl club have been studied extensively for its extreme impact tolerance, but a systematic in situ investigation on the multiscale mechanical responses under high-speed impact has not been reported. Here the full dynamic deformation and crack evolution process within projectile-impacted dactyl using combined fast 2D X-ray imaging and high-resolution ex situ tomography are revealed. The results show that hydration states can lead to significantly different toughening mechanisms inside dactyl under dynamic loading. A previously unreported 3D interlocking structural design in the impact surface and impact region is reported using nano X-ray tomography. Experimental results and dynamic finite-element modeling suggest this unique structure plays an important role in resisting catastrophic structural damage and hindering crack propagation. This work is a contribution to understanding the key toughening strategies of biological materials and provides valuable information for biomimetic manufacturing of impact-resistant materials in general.

X-ray zooming microscopy with two Fresnel zone plates

We propose a variable-magnification full-field x-ray microscope using two Fresnel zone plates (FZPs). By moving the positions of the two FZPs, the magnification can be continuously changed even if the sample and camera positions are fixed. It was demonstrated that the magnification can be changed in the range of 25-150× using a hard x-ray beam at 14.4 keV. Using the first FZP as a convex lens and the second FZP as a concave lens, high magnification can be achieved at a short camera length. Even under the condition of a camera length of about 7 m, a magnification higher than 300× was achieved, and a line and space pattern with a pitch of 40 nm was observed at 10 keV. By inserting a knife edge at an appropriate position in the optical system, a phase-contrast image can be easily obtained, which is useful for soft-tissue observation of biological samples.

Hard X-ray ptychography at Taiwan Photon Source at 11-20 nm spatial resolution

X-ray ptychography, a technique based on scanning and processing of coherent diffraction patterns, is a non-destructive imaging technique with a high spatial resolution far beyond the focused beam size. Earlier demonstrations of hard X-ray ptychography at Taiwan Photon Source (TPS) using an in-house program successfully recorded the ptychographic diffraction patterns from a gold-made Siemens star as a test sample and retrieved the finest inner features of 25 nm. Ptychography was performed at two beamlines with different focusing optics: a pair of Kirkpatrick-Baez mirrors and a pair of nested Montel mirrors, for which the beam sizes on the focal planes were 3 µm and 200 nm and the photon energies were from 5.1 keV to 9 keV. The retrieved spatial resolutions are 20 nm to 11 nm determined by the 10-90% line-cut method and half-bit threshold of Fourier shell correlation. This article describes the experimental conditions and compensation methods, including position correction, mixture state-of-probe, and probe extension methods, of the aforementioned experiments. The discussions will highlight the criteria of ptychographic experiments at TPS as well as the opportunity to characterize beamlines by measuring factors such as the drift or instability of beams or stages and the coherence of beams. Besides, probe functions, the full complex fields illuminated on samples, can be recovered simultaneously using ptychography. Theoretically, the wavefield at any arbitrary position can be estimated from one recovered probe function undergoing wave-propagating. The verification of probe-propagating has been carried out by comparing the probe functions obtained by ptychography and undergoing wave-propagating located at 0, 500 and 1000 µm relative to the focal plane.

Usefulness of microfocus computed tomography in life science research: preliminary study using murine micro-hepatic tumor models

PURPOSE

Microfocus computed tomography (micro-CT) has not been widely used at high radiation intensity (industrial micro-CT) in life science fields. In this preliminary study, we investigated its potential value in the detection of micro-hepatic tumors in a mouse model.

METHODS

The liver with micro-hepatic tumors was surgically resected en-bloc from mice, and examined with industrial micro-CT and lower intensity micro-CT (small animal micro-CT). The number of hepatic tumors was manually counted on serial images. Then, the accuracy of each technique was determined by preparing matching liver sections and comparing the number of tumors identified in a conventional pathological examination.

RESULTS

The number of hepatic tumors evaluated with industrial micro-CT showed high concordance with the results of the pathological examinations (intraclass correlation coefficient [ICC]: 0.984; 95% confidence interval [CI] 0.959-0.994). On the other hand, the number of hepatic tumors evaluated with the small animal micro-CT showed low concordance with the number identified in the pathological examinations (ICC: 0.533; 95% CI 0.181-0.815).

CONCLUSION

Industrial micro-CT improved the detection of small structures in resected specimens, and might be a promising solution for life science research.

High-energy x-ray nanotomography introducing an apodization Fresnel zone plate objective lens

In this study, high-energy x-ray nanotomography (nano-computed tomography, nano-CT) based on full-field x-ray microscopy was developed. Fine two-dimensional and three-dimensional (3D) structures with linewidths of 75 nm-100 nm were successfully resolved in the x-ray energy range of 15 keV-37.7 keV. The effective field of view was ∼60 µm, and the typical measurement time for one tomographic scan was 30 min-60 min. The optical system was established at the 250-m-long beamline 20XU of SPring-8 to realize greater than 100× magnification images. An apodization Fresnel zone plate (A-FZP), specifically developed for high-energy x-ray imaging, was used as the objective lens. The design of the A-FZP for high-energy imaging is discussed, and its diffraction efficiency distribution is evaluated. The spatial resolutions of this system at energies of 15 keV, 20 keV, 30 keV, and 37.7 keV were examined using a test object, and the measured values are shown to be in good agreement with theoretical values. High-energy x-ray nano-CT in combination with x-ray micro-CT is applied for 3D multiscale imaging. The entire bodies of bulky samples, ∼1 mm in diameter, were measured with the micro-CT, and the nano-CT was used for nondestructive observation of regions of interest. Examples of multiscale CT measurements involving carbon steel, mouse bones, and a meteorite are discussed.