Phase-contrast Imaging Research Articles

Cylindrical compression of thin wires by irradiation with a Joule-class short-pulse laser

Equation of state measurements at Jovian or stellar conditions are currently conducted by dynamic shock compression driven by multi-kilojoule multi-beam nanosecond-duration lasers. These experiments require precise design of the target and specific tailoring of the spatial and temporal laser profiles to reach the highest pressures. At the same time, the studies are limited by the low repetition rate of the lasers. Here, we show that by the irradiation of a thin wire with single-beam Joule-class short-pulse laser, a converging cylindrical shock is generated compressing the wire material to conditions relevant to the above applications. The shockwave was observed using Phase Contrast Imaging employing a hard X-ray Free Electron Laser with unprecedented temporal and spatial sensitivity. The data collected for Cu wires is in agreement with hydrodynamic simulations of an ablative shock launched by highly impulsive and transient resistive heating of the wire surface. The subsequent cylindrical shockwave travels toward the wire axis and is predicted to reach a compression factor of 9 and pressures above 800 Mbar. Simulations for astrophysical relevant materials underline the potential of this compression technique as a new tool for high energy density studies at high repetition rates.

A hybrid simulation method towards the gamma ray phase contrast imaging for metallic material

A high efficiency simulation method for propagation-based phase-contrast imaging, called directional macro-wavefront (DMWF), is developed with the aim of simulating high-energy phase-contrast imaging. This method takes both Monte Carlo and wave optical propagation into consideration. Traditional wave-optics-based simulation methods for phase-contrast imaging encounter unacceptable computational complexity when high-energy radiation is used. In contrast, this method effectively addresses this issue by using macro-wavefront integration. Several simulation examples using typical parameters of inverse Compton scattering sources are presented to illustrate the excellent energy adaptability and efficiency of the DMWF method. This method provides a more efficient approach for phase-contrast imaging simulations, which will drive the advancement of high-energy phase-contrast imaging.

Ortho-positronium lifetime for soft-tissue classification

The objective of this work is to showcase the ortho-positronium lifetime as a probe for soft-tissue characterization. We employed positron annihilation lifetime spectroscopy to experimentally measure the three components of the positron annihilation lifetime—para-positronium (p-Ps), positron, and ortho-positronium (o-Ps)—for three types of porcine, non-fixated soft tissues ex vivo: adipose, hepatic, and muscle. Then, we benchmarked our measurements with X-ray phase-contrast imaging, which is the current state-of-the-art for soft-tissue analysis. We found that the o-Ps lifetime in adipose tissues (2.54 ± 0.12 ns) was approximately 20% longer than in hepatic (2.04 ± 0.09 ns) and muscle (2.03 ± 0.12 ns) tissues. In addition, the separation between the measurements for adipose tissue and the other tissues was better from o-Ps lifetime measurement than from X-ray phase-contrast imaging. This experimental study proved that the o-Ps lifetime is a viable non-invasive probe for characterizing and classifying the different soft tissues. Specifically, o-Ps lifetime as a soft-tissue characterization probe had a strong sensitivity to the lipid content that can be potentially implemented in commercial positron emission tomography scanners that feature list-mode data acquisition.

Enhanced image transmission via four-wave mixing with assisted spiral phase contrast in hot atoms

Due to the diffusion motion of gas atoms, the object contour after frequency conversion becomes blurred, and the edge information of the image is always lost. Here, with assisted spiral phase contrast (SPC) imaging, we propose a scheme to preserve object edge information and enhance image recognition via four-wave mixing (FWM) process. The experimental results show that the algorithm can improve the structural similarity (SSIM) of object contour and the whole similarity of image. This work may contribute to the improvement of image quality via frequency conversion in atomic ensembles, and provides a possible implementation in all-optical image processing.

Micro to macro scale anatomical analysis of the human hippocampal arteries with synchrotron hierarchical phase-contrast tomography.

To date, no non-invasive imaging modality has been employed to profile the structural intricacies of the hippocampal arterial microvasculature in humans. We hypothesised that synchrotron-based imaging of the human hippocampus would enable precise characterisation of the arterial microvasculature. Two preserved human brains from, a 69-year-old female and a 63-year-old male body donors were imaged using hierarchical phase-contrast tomography (HiP-CT) with synchrotron radiation at multiple voxel resolutions from 25.08μm down to 2.45μm. Subsequent manual and semi-automatic artery segmentation were performed followed by morphometric analyses. These data were compared to published data from alternative methodologies. HiP-CT made it possible to segment in context the arterial architecture of the human hippocampus. Our analysis identified anterior, medial and posterior hippocampal arteries arising from the P2 segment of the posterior cerebral artery on the image slices. We mapped arterial branches with external diameters greater than 50 μm in the hippocampal region. We visualised vascular asymmetry and quantified arterial structures with diameters as small as 7 μm. Through the application of HiP-CT, we have provided the first imaging visualisation and quantification of the arterial system of the human hippocampus at high resolution in the context of whole brain imaging. Our results bridge the gap between anatomical and histological scales.

Self-healing spiral phase contrast imaging

Spiral phase contrast imaging alleviates the information load by extracting the geometric features of objects and is one of the most representative branches of instant imaging processing. The self-healing capacity of edge detectors can enhance their robustness to obstacles in practical applications. Here, a self-healing spiral phase contrast imaging scheme is proposed and experimentally demonstrated by a liquid crystal edge detector combining a spiral phase, an axicon phase, and a lens phase. The spiral phase is encoded into a liquid crystal by photopatterning. Self-healing contrast imaging is characterized by a series of edge images of both high-contrast amplitude-type and low-contrast phase-type objects. This work extends the self-healing capacity of these detectors to instant imaging processing and paves the way for optical applications with self-healing features.

Volumetric changes of the enteric nervous system under physiological and pathological conditions measured using x-ray phase-contrast tomography.

Full-thickness biopsies of the intestinal wall may be used to study and assess damage to the neurons of the enteric nervous system (ENS), that is, enteric neuropathy. The ENS is difficult to examine due to its localization deep in the intestinal wall and its organization with several connections in diverging directions. Histological sections used in clinical practice only visualize the sample in a two-dimensional way. X-ray phase-contrast micro-computed tomography (PC-μCT) has shown potential to assess the cross-sectional thickness and volume of the ENS in three dimensions (3D). The aim of this study was to explore the potential of PC-μCT to evaluate its use to determine the size of the ENS. Full-thickness biopsies of ileum obtained during surgery from five controls and six patients clinically diagnosed with enteric neuropathy and dysmotility were included. Punch biopsies of 1 mm in diameter and 1 cm in length, from an area containing myenteric plexus, were extracted from paraffin blocks, and scanned with synchrotron-based PC-μCT without any staining. The microscopic volumetric structure of the neural tissue (consisting of both ganglia and fascicles) could be determined in all samples. The ratio of neural tissue volume/total tissue volume was higher in controls than in patients with enteric neuropathy (P = 0.013). The patient with the longest disease duration had the lowest ratio. The assessment of neural tissue can be performed in an objective, standardized way, to ensure reproducibility and comparison under physiological and pathological conditions. Further evaluation is needed to examine the role of this method in the diagnosis of enteric neuropathy.

A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data.

Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time-dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)-based machine learning and autoencoder-type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD-based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise-free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine-learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

Grating-based spatial harmonic frequency X-ray imaging for qualitative and quantitative studies of granular and liquids materials

Grating-based spatial harmonic frequency X-ray imaging method allows the individual reconstruction of attenuation, scattering and phase-contrast X-ray images with a single exposure of the samples. In this work, the influence of the attenuation grating features (visibility and projected period) and sample features (thickness and grains sizes) on the reconstructed images were experimentally explored. Results showed that fringe visibility higher than 15% are fundamental for the proper image reconstruction. Even though reconstructed imagens are low spatial resolution (140–340 μm), they presented higher signal-to-noise ratio (SNR 85–700) than conventional images (SNR 14–39), due to the Fourier analyses procedure. Scattering could not resolve the individual grains structures, nevertheless, they revealed the presence of structures smaller than the image system resolution. Finally, it is presented that combining attenuation and scattering signals allows differentiation among the different grain sizes in granular materials and different liquids that appears similar in conventional images.

NeuroQuantify – An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning

BackgroundThe segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. New methodWe have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. ResultsNeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. Comparison with existing methodsNeuroQuantify overcomes some of the limitations of existing methods in the automatic and accurate analysis of neuronal structures. It has been developed for phase contrast images rather than fluorescence images. In addition to typical functionality of cell counting, NeuroQuantify also detects and counts neurites, measures the neurite lengths, and produces the neurite orientation distribution. ConclusionsWe offer a valuable tool to assess network development rapidly and effectively. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub at https://github.com/StanleyZ0528/neural-image-segmentation.

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys.

Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy. We organized a global machine learning competition, engaging 1,401 participants, to help develop new deep learning methods for 3D blood vessel segmentation. This paper presents a detailed analysis of the top-performing solutions using manually curated 3D Hierarchical Phase-Contrast Tomography datasets of the human kidney, focusing on the segmentation accuracy and morphological analysis, thereby establishing a benchmark for future studies in blood vessel segmentation within phase-contrast tomography imaging.

Differential phase contrast from electrons that cause inner shell ionization

Differential Phase Contrast (DPC) imaging, in which deviations in the bright field beam are in proportion to the electric field, has been extensively studied in the context of pure elastic scattering. Here we discuss differential phase contrast formed from core-loss scattered electrons, i.e. those that have caused inner shell ionization of atoms in the specimen, using a transition potential approach for which we study the number of final states needed for a converged calculation. In the phase object approximation, we show formally that differential phase contrast formed from core-loss scattered electrons is mainly a result of preservation of elastic contrast. Through simulation we demonstrate that whether the inelastic DPC images show element selective contrast depends on the spatial range of the ionization interaction, and specifically that when the energy loss is low the delocalisation can lead to contributions to the contrast from atoms other than that ionized. We further show that inelastic DPC images remain robustly interpretable to larger thicknesses than is the case for elastic DPC images, owing to the incoherence of the inelastic wavefields, though subtleties due to channelling remain. Lastly, we show that while a very high dose will be needed for sufficient counting statistics to discern differential phase contrast from core-loss scattered electrons, there is some enhancement of the signal-to-noise ratio with thickness that makes inelastic DPC imaging more achievable for thicker samples.

Computerized simulation of 2-dimensional phase contrast images using spiral phase plates in neutron interferometry

Abstract We present calculations of interferograms (interference patterns) of one or multiple spiral phase plates that would be observed with a perfect crystal neutron interferometer of Mach–Zehnder type. A spiral phase plate (SPP) in one of the two coherent beam paths produces a twist in the phase front and thus a vortex beam with intrinsic angular momentum, which in the case of neutrons should be observed as a characteristic interference pattern that appears complementary to each other in both detectors behind the interferometer. Adding additional SPPs in one beam path of the interferometer yield interference patterns similar to that of a single SPP but only due to the cumulative step height. All simulated interferograms have been calculated on the basis of dynamical neutron diffraction without any assumption of a neutron orbital angular momentum and show very convincing agreement with experimental results from the literature, see e.g. (C. W. Clark, R. Barankov, M. G. Huber, M. Arif, D. G. Cory, and D. A. Pushin, “Controlling neutron orbital angular momentum,” Nature, vol. 525, pp. 504–506, 2015). In particular, this clarifies, that the cited experiments do not give evidence of the quantization of interactions caused by a twist of the phase front of a neutron wave in the interferometer and thus no evidence for the effect of a neutron orbital angular momentum.

Thermomechanical and damage characterisation of short glass fibre reinforced polyamide 6 and impact-modified polyamide 6 composites

Polyamide 6 and its composites are widely used in engineering applications that are exposed to high strain rate deformation. This paper investigates the thermomechanical properties of two polyamide 6 composites, both reinforced with 30 wt% short glass fibres, and one of which additionally contains an impact modifier, to provide an understanding of the mechanical response over a wide range of strain rates and temperatures. Compression experiments were performed at rates between 2 and 3000 s−1, with high speed optical and infrared cameras to aid interpretation of the rate-dependent failure arising from the formation of adiabatic shear bands. Further high strain rate experiments were performed with ultra-fast X-ray phase-contrast imaging to provide in-situ internal damage evaluation. These data will improve the utilization of these composites and aid in development of advanced thermomechanical models.

11-Hydroxyeicosatetraenoics induces cellular hypertrophy in an enantioselective manner.

R/S enantiomers of 11-hydroxyeicosatertraenoic acid (11-HETE) are formed from arachidonic acid by enzymatic and non-enzymatic pathways. 11-HETE is predominately formed by the cytochrome P450 1B1 (CYP1B1). The role of CYP1B1 in the development of cardiovascular diseases is well established. This study aimed to assess the cellular hypertrophic effect of 11-HETE enantiomers in human RL-14 cardiomyocyte cell line and to examine their association with CYP1B1 levels. Human fetal ventricular cardiomyocyte, RL-14 cells, were treated with 20µM (R) or (S) 11-HETE for 24h. Thereafter, cellular hypertrophic markers and cell size were then determined using real-time polymerase chain reaction (RT-PCR) and phase-contrast imaging, respectively. The mRNA and protein levels of selected CYPs were determined using RT-PCR and Western blot, respectively. In addition, we examined the effect of (R) and (S) 11-HETE on CYP1B1 catalytic activity using human recombinant CYP1B1 and human liver microsomes. Both (R) and (S) 11-HETE induced cellular hypertrophic markers and cell surface area in RL-14 cells. Both enantiomers significantly upregulated CYP1B1, CYP1A1, CYP4F2, and CYP4A11 at the mRNA and protein levels, however, the effect of the S-enantiomer was more pronounced. Furthermore, 11(S)-HETE increased the mRNA and protein levels of CYP2J and CYP4F2, whereas 11(R)-HETE increased only CYP4F2. Only 11(S)-HETE significantly increased the catalytic activity of CYP1B1 in recombinant human CYP1B1, suggesting allosteric activation in an enantioselective manner. Our study provides the first evidence that 11-HETE can induce cellular hypertrophy in RL-14 cells via the increase in CYP1B1 mRNA, protein, and activity levels.

Standardization of a Novel Semi-Automatic Software for Neurite Outgrowth Measurement.

Effective live-imaging techniques are crucial to assess neuronal morphology in order to measure neurite outgrowth in real time. The proper measurement of neurite outgrowth has been a long-standing challenge over the years in the neuroscience research field. This parameter serves as a cornerstone in numerous in vitro experimental setups, ranging from dissociated cultures and organotypic cultures to cell lines. By quantifying the neurite length, it is possible to determine if a specific treatment worked or if axonal regeneration is enhanced in different experimental groups. In this study, the aim is to demonstrate the robustness and accuracy of the Incucyte Neurotrack neurite outgrowth analysis software. This semi-automatic software is available in a time-lapse microscopy system which offers several advantages over commonly used methodologies in the quantification of the neurite length in phase contrast images. The algorithm masks and quantifies several parameters in each image and returns neuronal cell metrics, including neurite length, branch points, cell-body clusters, and cell-body cluster areas. Firstly, we validated the robustness and accuracy of the software by correlating its values with those of the manual NeuronJ, a Fiji plug-in. Secondly, we used the algorithm which is able to work both on phase contrast images as well as on immunocytochemistry images. Using specific neuronal markers, we validated the feasibility of the fluorescence-based neurite outgrowth analysis on sensory neurons in vitro cultures. Additionally, this software can measure neurite length across various seeding conditions, ranging from individual cells to complex neuronal nets. In conclusion, the software provides an innovative and time-effective platform for neurite outgrowth assays, paving the way for faster and more reliable quantifications.

Generative AI for Cell Type-Specific Fluorescence Image Generation of hPSC-derived Cardiac Organoid.

Human pluripotent stem cell (hPSC)-derived cardiac organoid is the most recent three-dimensional tissue structure that mimics the structure and functionality of the human heart and plays a pivotal role in modeling heart development and disease. The hPSC-derived cardiac organoids are commonly characterized by bright-field microscopic imaging for tracking daily organoid differentiation and morphology formation. Although the brightfield microscope provides essential information about hPSC-derived cardiac organoids, such as morphology, size, and general structure, it does not extend our understanding of cardiac organoids on cell type-specific distribution and structure. Then, fluorescence microscopic imaging is required to identify the specific cardiovascular cell types in the hPSC-derived cardiac organoids by fluorescence immunostaining fixed organoid samples or fluorescence reporter imaging of live organoids. Both approaches require extra steps of experiments and techniques and do not provide general information on hPSC-derived cardiac organoids from different batches of differentiation and characterization, which limits the biomedical applications of hPSC-derived cardiac organoids. This research addresses this limitation by proposing a comprehensive workflow for colorizing phase contrast images of cardiac organoids from brightfield microscopic imaging using conditional Generative Adversarial Networks (GANs) to provide cardiovascular cell type-specific information in hPSC-derived cardiac organoids. By infusing these phase contrast images with accurate fluorescence colorization, our approach aims to unlock the hidden wealth of cell type, structure, and further quantifications of fluorescence intensity and area, for better characterizing hPSC-derived cardiac organoids.

Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning

Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast magnetic resonance imaging (PC-MRI) signal formed by the velocity of flowing CSF are represented as a set of velocity-encoded images or maps, which can be thought of as signal data in the context of medical imaging, enabling the evaluation of pulsatile patterns throughout a cardiac cycle. However, automatic segmentation of the CSF region in a PC-MRI image is challenging, and implementing an explained ML method using pulsatile data as a feature remains unexplored. This paper presents lightweight machine learning (ML) algorithms to perform CSF lumen segmentation in spinal, utilizing sets of velocity-encoded images or maps as a feature. The Dataset contains 57 PC-MRI slabs by 3T MRI scanner from control and idiopathic scoliosis participants are involved to collect data. The ML models are trained with 2176 time series images. Different cardiac periods image (frame) numbers of PC-MRIs are interpolated in the preprocessing step to align to features of equal size. The fivefold cross-validation procedure is used to estimate the success of the ML models. Additionally, the study focusses on enhancing the interpretability of the highest-accuracy eXtreme gradient boosting (XGB) model by applying the shapley additive explanations (SHAP) technique. The XGB algorithm presented its highest accuracy, with an average fivefold accuracy of 0.99% precision, 0.95% recall, and 0.97% F1 score. We evaluated the significance of each pulsatile feature's contribution to predictions, offering a more profound understanding of the model's behavior in distinguishing CSF lumen pixels with SHAP. Introducing a novel approach in the field, develop ML models offer comprehension into feature extraction and selection from PC-MRI pulsatile data. Moreover, the explained ML model offers novel and valuable insights to domain experts, contributing to an enhanced scholarly understanding of CSF dynamics.

Phase-contrast visualization of human tissues using superimposed wavefront imaging of diffraction-enhanced x-rays.

Phase-contrast computed tomography (CT) using high-brilliance, synchrotron-generated x-rays enable three-dimensional (3D) visualization of microanatomical structures within biological specimens, offering exceptionally high-contrast images of soft tissues. Traditional methods for phase-contrast CT; however, necessitate a gap between the subject and the x-ray camera, compromising spatial resolution due to penumbral blurring. Our newly developed technique, Superimposed Wavefront Imaging of Diffraction-enhanced x-rays (SWIDeX), leverages a Laue-case Si angle analyzer affixed to a scintillator to convert x-rays to visible light, capturing second-order differential phase contrast images and effectively eliminating the distance to the x-ray camera. This innovation achieves superior spatial resolution over conventional methods. In this paper, the imaging principle and CT reconstruction algorithm based on SWIDeX are presented in detail and compared with conventional analyzer-based imaging (ABI). It also shows the physical setup of SWIDeX that provides the resolution preserving second-order differential images for reconstruction. We compare the spatial resolution and the sensitivity of SWIDeX to conventional ABI. To demonstrate high-spatial resolution achievable by SWIDeX, the internal structures of four human tissues-ductal carcinoma in situ, normal stomach, normal pancreas, and intraductal papillary mucinous neoplasm of the pancreas-were visualized using an imaging system configured at the Photon Factory's BL14B beamline under the High Energy Accelerator Research Organization (KEK). Each tissue was thinly sliced after imaging, stained with hematoxylin and eosin (H&E) for conventional microscope-based pathology. A comparison of SWIDeX-CT and pathological images visually demonstrates the effectiveness of SWIDeX-CT for biological tissue imaging. SWIDeX could generate clearer 3D images than existing analyzer-based phase-contrast methods and accurately delineate tissue structures, as validated against histopathological images. SWIDeX can visualize important 3D structures in biological soft tissue with high spatial resolution and can be an important tool for providing information between the disparate scales of clinical and pathological imaging.

High resolution X-ray phase contrast imaging for non-destructive testing of composite materials

X-ray imaging is widely used in non-destructive testing dedicated to industry, medical or security domain. In aircraft production, the aerospace industry increasingly integrates carbon fibre reinforced polymers (CFRP), which may develop defects from operational wear, like lightning strikes. To robustly characterize density changes resulting from induced lightning damage in CFRP, multilateral shearing interferometry (MLSI) – a phase imaging technique – have been incorporated on a X-ray laboratory bench. Indeed, such method provides higher contrasts than X-ray attenuation imaging for composite materials and brings complementary information of the imaged sample. However, extracting the phase signal requires conditions of the imaging system and algorithmic strategies, which can lead to the emergence of artifacts in phase images. This thesis work involved the characterisation of these artifacts in the context of MLSI applied to X-ray phase imaging, as well as the development of tracking methods and corrective strategies.