Phase-contrast Imaging Research Articles

Tangentially viewing phase contrast imaging for turbulence measurementsin toroidal fusion devices

Phase contrast imaging (PCI) is an established and powerful techniquefor measuring density fluctuations in plasmas and has been successfullyapplied to several fusion devices. Rooted in a concept first developed formicroscopy, PCI belongs to the category of internal-reference interferometersand has been shown to possess superior qualities among such techniques,particularly in terms of spatial linearity. In essence, it produces a true imageof fluctuations in the plane perpendicular to the propagation direction of theprobing laser beam, provided their characteristic spatial scale is smaller thanthe beam width. The measurement in itself is line-integrated and thus notspatially resolved longitudinally to the beam. However, the properties of theturbulence itself can be exploited to achieve longitudinal resolution,particularly when the beam propagates nearly tangentially to the magnetic field.This assertion has been recently rigorously tested through numericalmodeling, which has revealed significant additional complexity while confirmingthe general principle. Tangential PCI has been employed extensively in the TCVtokamak and has resulted in a rich body of work on broadband microturbulence inthe ion-temperature-gradient/trapped-electron-moderange and on geodesic acoustic modes. A similar diagnosticarrangement is also at an advanced planning stage for the new superconductingtokamak JT-60SA.

Study of Phase Imaging Contrast Optimization in Tapping Mode Atomic Force Microscopy

In tapping mode atomic force microscopy (TM‐AFM), the phase image can reveal more about the physicochemical properties of the sample surface than the topography image, making it widely utilized in fields such as materials science and life sciences. However, obtaining high phase contrast in phase images during experiments is challenging, thus studying the phase imaging theory of TM‐AFM and optimizing phase contrast methods hold significant importance. This paper derives a theoretical expression for phase contrast based on phase theory, discovering that phase contrast is directly related to the frequency ratio and background dissipation quality factor. Through theoretical, experimental, and simulation approaches, optimization methods for phase contrast were proposed from the perspectives of frequency ratio and background dissipation quality factor as follows: (1) By fitting experimental results with the theoretical expression for phase contrast, the optimal frequency ratio for scanning can be determined, and imaging with this optimal ratio can enhance phase contrast and (2) reducing the width ratio between the free and fixed ends of the probe can decrease background dissipation, increase the proportion of core dissipation in the total energy dissipation of the probe, and thereby improve phase imaging contrast. These findings are significant in guiding TM‐AFM phase scanning experiments and optimizing phase imaging contrast.

Cardiac Magnetic Resonance Imaging in Heart Failure

Heart failure (HF) is a clinical syndrome with a wide variety of clinical presentations, pathophysiologies, and natural histories. HF is becoming more prevalent globally, thus increasing effects on healthcare systems. Cardiac magnetic resonance (CMR) imaging is a valuable tool for better understanding HF and its prognosis. The commonly used reference standard of CMR cine imaging provides accurate assessment of chamber size and function. Phase contrast imaging can be used to assess the degree of valvular regurgitation and complex flow patterns. Stress perfusion imaging can detect subtle areas of ischemia and microvascular dysfunction. Late gadolinium enhancement imaging aids in diagnosing causes of HF and guiding revascularization in ischemic cardiomyopathy. Supported by histological validation, T1 and T2 mapping provides non-invasive information on tissue characteristics in inflammatory and infiltrative cardiomyopathies. CMR also provides various techniques to measure strain in the atria and ventricles at high spatial and temporal resolution. CMR continues to serve as an important modality for the evaluation of HF.

Pattern-illumination time-resolved phase microscopy and its applications for photocatalytic and photovoltaic materials.

Pattern-illumination time-resolved phase microscopy (PI-PM) is a technique used to study the microscopic charge carrier dynamics in photocatalytic and photovoltaic materials. The method involves illuminating a sample with a pump light pattern, which generates charge carriers and they decay subsequently due to trapping, recombination, and transfer processes. The distribution of photo-excited charge carriers is observed through refractive index changes using phase-contrast imaging. In the PI-PM method, the sensitivity of the refractive index change is enhanced by adjusting the focus position, the method takes advantage of photo-excited charge carriers to observe non-radiative processes, such as charge diffusion, trapping in defect/surface states, and interfacial charge transfer of photocatalytic and photovoltaic reactions. The quality of the image sequence is recovered using various informatics calculations. Categorizing and mapping different types of charge carriers based on their response profiles using clustering analysis provides spatial information on charge carrier types and the identification of local sites for efficient and inefficient photo-induced reactions, providing valuable information for the design and optimization of photocatalytic materials such as the cocatalyst effect.

Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.

Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image features. Nevertheless, rapidly growing artificial intelligence technology is overcoming obstacles. We previously reported the fine-tuned apodized phase-contrast microscopy system to capture high-resolution, label-free images of organelle dynamics in unstained living cells (Shimasaki, K. et al. (2024). Cell Struct. Funct., 49: 21-29). We here showed machine learning-based segmentation models for subcellular targeted objects in phase-contrast images using fluorescent markers as origins of ground truth masks. This method enables accurate segmentation of organelles in high-resolution phase-contrast images, providing a practical framework for studying cellular dynamics in unstained living cells.Key words: label-free imaging, organelle dynamics, apodized phase contrast, deep learning-based segmentation.

Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.

Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed via LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.

A microfluidic system for the cultivation of cyanobacteria with precise light intensity and CO2 control: enabling growth data acquisition at single-cell resolution.

Quantification of cell growth is central to any study of photoautotrophic microorganisms. However, cellular self-shading and limited CO2 control in conventional photobioreactors lead to heterogeneous conditions that obscure distinct correlations between the environment and cellular physiology. Here we present a microfluidic cultivation platform that enables precise analysis of cyanobacterial growth with spatio-temporal resolution. Since cyanobacteria are cultivated in monolayers, cellular self-shading does not occur, allowing homogeneous illumination and precise knowledge of the photon-flux density at single-cell resolution. A single chip contains multiple channels, each connected to several hundred growth chambers. In combination with an externally applied light gradient, this setup enables high-throughput multi-parameter analysis in short time. In addition, the multilayered microfluidic design allows continuous perfusion of defined gas mixtures. Transversal CO2 diffusion across the intermediate polydimethylsiloxane membrane results in homogeneous CO2 supply, with a unique exchange-surface to cultivation-volume ratio. Three cyanobacterial model strains were examined under various, static and dynamic environmental conditions. Phase-contrast and chlorophyll fluorescence images were recorded by automated time-lapse microscopy. Deep-learning trained cell segmentation was used to efficiently analyse large image stacks, thereby generating statistically reliable data. Cell division was highly synchronized, and growth was robust under continuous illumination but stopped rapidly upon initiating dark phases. CO2-Limitation, often a limiting factor in photobioreactors, was only observed when the device was operated under reduced CO2 between 50 and 0 ppm. Here we provide comprehensive and precise data on cyanobacterial growth at single-cell resolution, accessible for further growth studies and modeling.

Machine-learning-guided quantitative delineation of cell morphological features and responses to nanomaterials.

Delineation of cell morphological features is essential to decipher cell responses to external stimuli like theranostic nanomaterials. Conventional methods rely on labeled approaches, such as fluorescence imaging and flow cytometry, to assess cell responses. Besides potentially perturbing cell structure and morphology, these approaches are relatively complex, time-consuming, expensive, and may not be compatible with downstream analysis involving live cells. Herein, leveraging label-free phase-contrast or brightfield microscopy imaging and machine learning, the delineation of different cell types, phenotypes, and states for monitoring live cell responses is reported. Notably, pixel classification based on a supervised random forest classifier is used to distinguish between cells and backgrounds from the microscopy images, followed by cell segmentation and morphological feature extraction. Quantitative analysis shows that most of the compared cell groups have distinguishable size and shape features. Principal component analysis and unsupervised k-means clustering of morphological features reveal the possible existence of heterogenous cell subpopulations and treatment responses among the seemingly homogenous cell groups. This shows the merit of the reported approach in complementing conventional techniques for cell analysis. It is anticipated that the demonstrated method will further aid the implementation of machine learning to streamline the analysis of cell morphology and responses for early disease diagnosis and treatment response monitoring.

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging

BackgroundTime-resolved, three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow MRI segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies. MethodsThe study population consists of 260 4D flow MRI datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced 3D U-net convolutional neural network (CNN) architecture with residual units was trained for 2D+t aortic cross-sectional segmentation. The model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation. ResultsThe representation of technical factors such as scanner vendor and field strength in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients’ datasets performed well on datasets of healthy subjects but not vice versa. ConclusionThis study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow MRI, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.

Diversity of sound production and hearing in fishes: Exploring the riddles of communication and sensory biology

My research in fish bioacoustics started in the mid-1980s and comprised the diversity in sound-generating mechanisms and sound production in various contexts such as dyadic contests. I worked on European freshwater and marine fish, but mainly on tropical fish from Southeast Asia (gouramis) and the Amazonian River system (pimelodid, doradid catfishes, piranhas). The diversity of sonic organs raised the question if general patterns of innervation of sonic muscles in vocal fishes exist. Different vocal (sonic) motor nuclei were identified in the brainstem and rostral spinal cord for pectoral and swim bladder vibration mechanisms. In addition, I wanted to find out if vocal fish differ from nonvocal species in hearing abilities. Measurements applying the auditory evoked potential technique revealed a large diversity of auditory sensitivities depending on the presence or lack of accessory hearing structures independently of sound production. Furthermore, I investigated the anatomy, ultrastructure of inner ears, and hearing enhancement by auxiliary hearing structures. Recently, we studied sound-induced motion of swim bladders, auditory ossicles, and otoliths using x-ray phase contrast imaging. A large part of my studies was carried out in cooperation with laboratories in the USA, Germany, Italy, Portugal, and Brasilia which made my research interesting.

Kiosk 2R-FB-03 - Feasibility and Reproducibility of Measuring Myocardial Performance Index Using 2D Phase Contrast Imaging in Pediatric Congenital Heart Disease

Kiosk 2R-FB-03 - Feasibility and Reproducibility of Measuring Myocardial Performance Index Using 2D Phase Contrast Imaging in Pediatric Congenital Heart Disease

Immuno-surveillance and protection of the human cochlea.

Despite its location near infection-prone areas, the human inner ear demonstrates remarkable resilience. This suggests that there are inherent instruments deterring the invasion and spread of pathogens into the inner ear. Here, we combined high-resolution light microscopy, super-resolution immunohistochemistry (SR-SIM) and synchrotron phase contrast imaging (SR-PCI) to identify the protection and barrier systems in the various parts of the human inner ear, focusing on the lateral wall, spiral ganglion, and endolymphatic sac. Light microscopy was conducted on mid-modiolar, semi-thin sections, after direct glutaraldehyde/osmium tetroxide fixation. The tonotopic locations were estimated using SR-PCI and 3D reconstruction in cadaveric specimens. The sections were analyzed for leucocyte and macrophage activity, and the results were correlated with immunohistochemistry using confocal microscopy and SR-SIM. Light microscopy revealed unprecedented preservation of cell anatomy and several macrophage-like cells that were localized in the cochlea. Immunohistochemistry demonstrated IBA1 cells frequently co-expressing MHC II in the spiral ganglion, nerve fibers, lateral wall, spiral limbus, and tympanic covering layer at all cochlear turns as well as in the endolymphatic sac. RNAscope assays revealed extensive expression of fractalkine gene transcripts in type I spiral ganglion cells. CD4 and CD8 cells occasionally surrounded blood vessels in the modiolus and lateral wall. TMEM119 and P2Y12 were not expressed, indicating that the cells labeled with IBA1 were not microglia. The round window niche, compact basilar membrane, and secondary spiral lamina may form protective shields in the cochlear base. The results suggest that the human cochlea is surveilled by dwelling and circulating immune cells. Resident and blood-borne macrophages may initiate protective immune responses via chemokine signaling in the lateral wall, spiral lamina, and spiral ganglion at different frequency locations. Synchrotron imaging revealed intriguing protective barriers in the base of the cochlea. The role of the endolymphatic sac in human inner ear innate and adaptive immunity is discussed.

Kiosk 3R-TA-03 - Utilization of T2 Prep for MR Oximetery Combined with 4D Flow Phase Contrast Imaging for Evaluation of Extracardiac Organ Oxygen Metabolism in Fontan Patients

Kiosk 3R-TA-03 - Utilization of T2 Prep for MR Oximetery Combined with 4D Flow Phase Contrast Imaging for Evaluation of Extracardiac Organ Oxygen Metabolism in Fontan Patients

Fabrication of small-period neutron absorption grating by pressurized particle filling method

Neutron absorption gratings play a crucial role in neutron phase contrast imaging systems, where the fabrication of large-size and small-period absorption gratings that meet imaging requirements presents a significant challenge. The pressurized particle filling method has been successfully applied to fabricate large-size absorption gratings. Here, we investigated the feasibility of the pressurized particle filling method for fabricating small-period gratings and proposed an optimized pressurized particle filling method. The grating surface was covered with a uniform particle layer and then pressurized, utilizing the adaptive deformation of the particle layer to achieve uniform particle filling. Neutron absorption gratings with an area of 60 × 60 mm2 and periods of 8 and 4 µm were fabricated through this method. The particle filling rate and fabrication efficiency were successfully improved. In addition, the evaluation of the particle filling uniformity method by analyzing the proportion of particles on the grating surface was proposed. The better uniformity of small-period neutron absorption gratings indicated that the optimized pressurized particle filling method can achieve relatively uniform particle filling.

Modeling of X-Ray Phase Contrast Imaging of Optically Heterogeneous Objects

Modeling of X-Ray Phase Contrast Imaging of Optically Heterogeneous Objects

High Throughput Tomography (HiTT) on EMBL beamline P14 on PETRA III.

Here, high-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany, is presented. A high-photon-flux undulator beamline is used to perform tomographic phase-contrast acquisition in about two minutes which is linked to an automated data processing pipeline that delivers a 3D reconstructed data set less than a minute and a half after the completion of the X-ray scan. Combining this workflow with a sophisticated robotic sample changer enables the streamlined collection and reconstruction of X-ray imaging data from potentially hundreds of samples during a beam-time shift. HiTT permits optimal data collection for many different samples and makes possible the imaging of large sample cohorts thus allowing population studies to be attempted. The successful application of HiTT on various soft tissue samples in both liquid (hydrated and also dehydrated) and paraffin-embedded preparations is demonstrated. Furthermore, the feasibility of HiTT to be used as a targeting tool for volume electron microscopy, as well as using HiTT to study plant morphology, is demonstrated. It is also shown how the high-throughput nature of the work has allowed large numbers of `identical' samples to be imaged to enable statistically relevant sample volumes to be studied.

Automated tracking of cell migration in phase contrast images with CellTraxx

The ability of cells to move and migrate is required during development, but also in the adult in processes such as wound healing and immune responses. In addition, cancer cells exploit the cells’ ability to migrate and invade to spread into nearby tissue and eventually metastasize. The majority of cancer deaths are caused by metastasis and the process of cell migration is therefore intensively studied. A common way to study cell migration is to observe cells through an optical microscope and record their movements over time. However, segmenting and tracking moving cells in phase contrast time-lapse video sequences is a challenging task. Several tools to track the velocity of migrating cells have been developed. Unfortunately, most of the automated tools are made for fluorescence images even though unlabelled cells are often preferred to avoid phototoxicity. Consequently, researchers are constrained with laborious manual tracking tools using ImageJ or similar software. We have therefore developed a freely available, user-friendly, automated tracking tool called CellTraxx. This software makes it easy to measure the velocity and directness of migrating cells in phase contrast images. Here, we demonstrate that our tool efficiently recognizes and tracks unlabelled cells of different morphologies and sizes (HeLa, RPE1, MDA-MB-231, HT1080, U2OS, PC-3) in several types of cell migration assays (random migration, wound healing and cells embedded in collagen). We also provide a detailed protocol and download instructions for CellTraxx.

Investigation of an Improved Angular Spectrum Method Based on Holography

Digital holography (DH) is a novel, real-time, non-destructive, and quantitative phase-contrast imaging method that is particularly suitable for label-free live biological cell imaging and real-time dynamic monitoring. It is currently a research hotspot in the interdisciplinary field of optics and biomedical sciences, both domestically and internationally. This article proposes an improved angle spectrum algorithm based on holographic technology, which reconstructs a cellular hologram based on phase information. Optical images and chromosome cell images, reconstructed using holographic technology at different diffraction distances under the improved angle spectrum algorithm, were analyzed and compared. The optimal diffraction distance for reconstructing chromosome cell images was selected, and chromosome cell images reproduced using traditional angle spectrum algorithms, angle spectrum algorithms combined with GS, and improved angle spectrum algorithms were compared. Comparative experiments with the different models show that the proposed algorithm is superior to traditional angle spectrum algorithms in reconstructing cell images based on phase information. Furthermore, experiments have shown that images reconstructed using the improved algorithm can resolve high signal-to-noise ratio information. This algorithmic improvement provides new applications for cellular detection in clinical diagnostics and is more suitable for cell phase reconstruction in practical applications.

Two-dimensional phase-contrast MRI reveals changes in uterine arterial blood flow in pregnant women administered tadalafil for fetal growth restriction

IntroductionWe aimed to examine the effect of uterine arterial (UtA) blood flow changes after tadalafil treatment for fetal growth restriction (FGR) using two-dimensional (2D) phase-contrast magnetic resonance imaging (PC-MRI). MethodsWe recruited 14 pregnant women with FGR aged 20–44 years, at ≥20 weeks’ gestation, between May 2019 and July 2020. They underwent 2D PC-MRI for UtA blood flow measurement 3 days (interquartile range: 2–4) after diagnosis. This group (FGR group) was compared with 14 gestational age (GA)-matched healthy pregnant women (control group). Six patients in the FGR group received treatment with tadalafil administered at 20 mg twice daily after the first MRI until delivery. They underwent a second MRI a week later. ResultsThe median total UtA blood/body surface area was 420 mL/min/m2 (290–494) in the FGR group and 547 mL/min/m2 (433–681) in the control group (p = 0.01). Percent increase in blood flow were significantly different between the FGR cases treated with tadalafil and control at 15.8 % (14.3–21.3) and 4.2 % (3.6–8.7), respectively (p = 0.03). DiscussionUtA blood flow in pregnant women with FGR was significantly lower than that in healthy pregnant women. Tadalafil is expected to improve UtA blood flow, thereby improving placental function in pregnant patients with FGR.

Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development

Organoids, which can reproduce the complex tissue structures found in embryos, are revolutionizing basic research and regenerative medicine. In order to use organoids for research and medicine, it is necessary to assess the composition and arrangement of cell types within the organoid, i.e., spatial gene expression. However, current methods are invasive and require gene editing and immunostaining. In this study, we developed a non-invasive estimation method of spatial gene expression patterns using machine learning. A deep learning model with an encoder-decoder architecture was trained on paired datasets of phase-contrast and fluorescence images, and was applied to a retinal organoid derived from mouse embryonic stem cells, focusing on the master gene Rax (also called Rx), crucial for eye field development. This method successfully estimated spatially plausible fluorescent patterns with appropriate intensities, enabling the non-invasive, quantitative estimation of spatial gene expression patterns within each tissue. Thus, this method could lead to new avenues for evaluating spatial gene expression patterns across a wide range of biology and medicine fields.