Exploring the interior of 3D endoluminal lesions in the air spaces by a novel electronic biopsy technique: A preliminary study of endoluminal colon lesions.

To explore the interior of a lesion in a 3D endoluminal view, this study investigates the application of an 'electronic biopsy' (EB) technique to computed tomographic colonography (CTC) for further differentiation and 2D image correlation of endoluminal lesions in the air spaces. A retrospective study of sixty-two various endoluminal lesions from thirty patients (13 males, 17 females; age range, 31 to 90 years) was approved by our institutional review board and evaluated. The endoluminal lesions were segmented using gray-level threshold and reconstructed into isosurfaces using a marching cube algorithm. EB allows users to interactively erode and apply grey-level mapping (GM) to the surface of the region of interest (ROI) in 3D CTC. Radiologists conducted the clinical evaluation, and the resulting data were analyzed. EB significantly improves 3D gray-level presentation for evaluating the surface and inside of endoluminal lesions over that of SR, GM or target GM (TGM) (P < 0.01) with preservation of the 3D spatial effect. Moreover, 3D to 2D image correlation were achieved in any layer of the lesion using EB as did GM/TGM on the surface. The specificity and diagnostic accuracy of EB are significantly greater than those of SR (P < 0.01). These performance can be better further with GM/TGM and reach the best with EB (specificity, 89.3-92.9%; accuracy, 95.2-96.8%). EB can be used in CTC to improve the differentiation of endoluminal lesions. EB increases 3D to 2D image correlations of the lesions on or beneath the lesion surface.

Confocal interferometric scattering microscopy reveals 3D nanoscopic structure and dynamics in live cells.

Bright-field light microscopy and related phase-sensitive techniques play an important role in life sciences because they provide facile and label-free insights into biological specimens. However, lack of three-dimensional imaging and low sensitivity to nanoscopic features hamper their application in many high-end quantitative studies. Here, we demonstrate that interferometric scattering (iSCAT) microscopy operated in the confocal mode provides unique label-free solutions for live-cell studies. We reveal the nanometric topography of the nuclear envelope, quantify the dynamics of the endoplasmic reticulum, detect single microtubules, and map nanoscopic diffusion of clathrin-coated pits undergoing endocytosis. Furthermore, we introduce the combination of confocal and wide-field iSCAT modalities for simultaneous imaging of cellular structures and high-speed tracking of nanoscopic entities such as single SARS-CoV-2 virions. We benchmark our findings against simultaneously acquired fluorescence images. Confocal iSCAT can be readily implemented as an additional contrast mechanism in existing laser scanning microscopes. The method is ideally suited for live studies on primary cells that face labeling challenges and for very long measurements beyond photobleaching times.

Performance of spiral UTE-MRI of the lung in post-COVID patients.

Patients recovered from COVID-19 may develop long-COVID symptoms in the lung. For this patient population (post-COVID patients), they may benefit from longitudinal, radiation-free lung MRI exams for monitoring lung lesion development and progression. The purpose of this study was to investigate the performance of a spiral ultrashort echo time MRI sequence (Spiral-VIBE-UTE) in a cohort of post-COVID patients in comparison with CT and to compare image quality obtained using different spiral MRI acquisition protocols. Lung MRI was performed in 36 post-COVID patients with different acquisition protocols, including different spiral sampling reordering schemes (line in partition or partition in line) and different breath-hold positions (inspiration or expiration). Three experienced chest radiologists independently scored all the MR images for different pulmonary structures. Lung MR images from spiral acquisition protocol that received the highest image quality scores were also compared against corresponding CT images in 27 patients for evaluating diagnostic image quality and lesion identification. Spiral-VIBE-UTE MRI acquired with the line in partition reordering scheme in an inspiratory breath-holding position achieved the highest image quality scores (score range = 2.17-3.69) compared to others (score range = 1.7-3.29). Compared to corresponding chest CT images, three readers found that 81.5% (22 out of 27), 81.5% (22 out of 27) and 37% (10 out of 27) of the MR images were useful, respectively. Meanwhile, they all agreed that MRI could identify significant lesions in the lungs. The Spiral-VIBE-UTE sequence allows for fast imaging of the lung in a single breath hold. It could be a valuable tool for lung imaging without radiation and could provide great value for managing different lung diseases including assessment of post-COVID lesions.

Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization.

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.

Self-gated 3D stack-of-spirals UTE pulmonary imaging at 0.55T.

PURPOSE: To develop an isotropic high-resolution stack-of-spirals UTE sequence for pulmonary imaging at 0.55 Tesla by leveraging a combination of robust respiratory-binning, trajectory correction, and concomitant-field corrections. METHODS: A stack-of-spirals golden-angle UTE sequence was used to continuously acquire data for 15.5 minutes. The data was binned to a stable respiratory phase based on superoinferior readout self-navigator signals. Corrections for trajectory errors and concomitant field artifacts, along with image reconstruction with conjugate gradient SENSE, were performed inline within the Gadgetron framework. Finally, data were retrospectively reconstructed to simulate scan times of 5, 8.5, and 12 minutes. Image quality was assessed using signal-to-noise, image sharpness, and qualitative reader scores. The technique was evaluated in healthy volunteers, patients with coronavirus disease 2019 infection, and patients with lung nodules. RESULTS: The technique provided diagnostic quality images with parenchymal lung SNR of 3.18 ± 0.0.60, 4.57 ± 0.87, 5.45 ± 1.02, and 5.89 ± 1.28 for scan times of 5, 8.5, 12, and 15.5 minutes, respectively. The respiratory binning technique resulted in significantly sharper images (p < 0.001) as measured with relative maximum derivative at the diaphragm. Concomitant field corrections visibly improved sharpness of anatomical structures away from iso-center. The image quality was maintained with a slight loss in SNR for simulated scan times down to 8.5 minutes. Inline image reconstruction and artifact correction were achieved in <5 minutes. CONCLUSION: The proposed pulmonary imaging technique combined efficient stack-of-spirals imaging with robust respiratory binning, concomitant field correction, and trajectory correction to generate diagnostic quality images with 1.75 mm isotropic resolution in 8.5 minutes on a high-performance 0.55 Tesla system.

Technologies Enabling Single-Molecule Super-Resolution Imaging of mRNA.

The transient nature of RNA has rendered it one of the more difficult biological targets for imaging. This difficulty stems both from the physical properties of RNA as well as the temporal constraints associated therewith. These concerns are further complicated by the difficulty in imaging endogenous RNA within a cell that has been transfected with a target sequence. These concerns, combined with traditional concerns associated with super-resolution light microscopy has made the imaging of this critical target difficult. Recent advances have provided researchers the tools to image endogenous RNA in live cells at both the cellular and single-molecule level. Here, we review techniques used for labeling and imaging RNA with special emphases on various labeling methods and a virtual 3D super-resolution imaging technique.

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.

Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool.

BACKGROUND: Efficient tools allowing the extraction of 2D surfaces from 3D-microscopy data are essential for studies aiming to decipher the complex cellular choreography through which epithelium morphogenesis takes place during development. Most existing methods allow for the extraction of a single and smooth manifold of sufficiently high signal intensity and contrast, and usually fail when the surface of interest has a rough topography or when its localization is hampered by other surrounding structures of higher contrast. Multiple surface segmentation entails laborious manual annotations of the various surfaces separately. RESULTS: As automating this task is critical in studies involving tissue-tissue or tissue-matrix interaction, we developed the Zellige software, which allows the extraction of a non-prescribed number of surfaces of varying inclination, contrast, and texture from a 3D image. The tool requires the adjustment of a small set of control parameters, for which we provide an intuitive interface implemented as a Fiji plugin. CONCLUSIONS: As a proof of principle of the versatility of Zellige, we demonstrate its performance and robustness on synthetic images and on four different types of biological samples, covering a wide range of biological contexts.

Three-Dimensional Visualization of Viral Structure, Entry, and Replication Underlying the Spread of SARS-CoV-2.

The global spread of SARS-CoV-2 has proceeded at an unprecedented rate. Remarkably, characterization of the virus using modern tools in structural biology has also progressed at exceptional speed. Advances in electron-based imaging techniques, combined with decades of foundational studies on related viruses, have enabled the research community to rapidly investigate structural aspects of the novel coronavirus from the level of individual viral proteins to imaging the whole virus in a native context. Here, we provide a detailed review of the structural biology and pathobiology of SARS-CoV-2 as it relates to all facets of the viral life cycle, including cell entry, replication, and three-dimensional (3D) packaging based on insights obtained from X-ray crystallography, cryo-electron tomography, and single-particle cryo-electron microscopy. The structural comparison between SARS-CoV-2 and the related earlier viruses SARS-CoV and MERS-CoV is a common thread throughout this review. We conclude by highlighting some of the outstanding unanswered structural questions and underscore areas that are under rapid current development such as the design of effective therapeutics that block viral infection.

2D Anatomical Structure for COVID-19 Medical Images.

For many clinical goals like surgical planning and radiotherapy treatment planning is necessary to understand the anatomical structures of the organ that is targeted. At the same time the 2D/3D shape of the organ is important to be reconstructed for the benefit of the doctors. For that reason, accurate segmentation techniques must be proposed to overcome the big data medical image storage problem. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

Head CT Image Segmentation and Three-Dimensional Reconstruction Technology Based on Human Anatomy.

With the continuous development of computer science and technology, the level of medical image processing and analysis technology has been significantly improved. In order to further optimize the medical imaging technology and provide assistance for medical diagnosis and treatment, this study will explore the head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy, using two morphological operation methods of image expansion and image corrosion, as well as the triangulation method based on surface contour, Optimize CT image segmentation technology and three-dimensional reconstruction technology. The results show that the CT image segmentation technology based on human anatomy can obtain the more essential morphology and features of the target image, and significantly improve the image quality. The size of the threshold can have a certain impact on the 3D reconstruction effect and reconstruction time to a certain extent. The larger the threshold, the shorter the reconstruction time, but the worse the 3D reconstruction effect. This shows that the target image after fitting has a good reconstruction effect, but the threshold level should be kept at a low level. The head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy have good application effects and can be popularized and applied in clinical diagnosis and treatment.

A preliminary study into the impact of using three-dimensional models in forensic anthropology learning and teaching.

Forensic anthropology has traditionally relied on two-dimensional (2D) images, such as photographs and sketches, to perform analyses, and disseminate findings. However, as 3D imaging technology advances, it has become more widely implemented into forensic anthropology analysis and practice. Teaching and learning in forensic anthropology still often relies on 2D images, but increasingly three-dimensional (3D) models are available to be used by students training in anatomy and osteology. Additionally, 3D models have been found to be beneficial to comprehension in other contexts within forensic anthropology, such as in the courtroom. The use of these models in the teaching of forensic anthropology is not yet widely implemented and more importantly, the impact on learning is not yet understood. The use of 3D imaging and visualisation in other educational contexts has seen positive results, for example in medical training. To explore this further, a study was conducted using an online activity to compare the comprehension scores of students educated using 2D textbook style images or 3D models on Sketchfab. The results showed that the use of 3D images was not detrimental to comprehension. Students using the 3D models were more consistent in their performance and reported an increase in confidence regardless of prior experience. The results of this study are of particular importance when distance learning due to the COVID-19 pandemic which means that students cannot always learn in a laboratory environment.

HYBRiD: hydrogel-reinforced DISCO for clearing mammalian bodies.

The recent development of solvent- and polymer-based brain-clearing techniques has advanced our ability to visualize the mammalian nervous system in three dimensions. However, it remains challenging to image the mammalian body en bloc. Here we developed HYBRiD (hydrogel-based reinforcement of three-dimensional imaging solvent-cleared organs (DISCO)), by recombining components of organic- and polymer-based clearing pipelines. We achieved high transparency and protein retention, as well as compatibility with direct fluorescent imaging and immunostaining in cleared mammalian bodies. Using parvalbumin- and somatostatin-Cre models, we demonstrated the utility of HYBRiD for whole-body imaging of genetically encoded fluorescent reporters without antibody enhancement of signals in newborn and juvenile mice. Using K18-hACE2 transgenic mice, HYBRiD enabled perfusion-free clearing and visualization of SARS-CoV-2 infection in a whole mouse chest, revealing macroscopic and microscopic features of viral pathology in the same sample. HYBRiD offers a simple and universal solution to visualize large heterogeneous body parts or entire animals for basic and translational research.

3D modelling and simulation of the dispersion of droplets and drops carrying the SARS-CoV-2 virus in a railway transport coach.

Computational fluid dynamics (CFD) modelling and 3D simulations of the air flow and dispersion of droplets or drops in semi-confined ventilated spaces have found topical applications with the unfortunate development of the Covid-19 pandemic. As an illustration of this scenario, we have considered the specific situation of a railroad coach containing a seated passenger infected with the SARS-CoV-2 virus (and not wearing a face mask) who, by breathing and coughing, releases droplets and drops that contain the virus and that present aerodynamic diameters between 1 and 1000 µm. The air flow is generated by the ventilation in the rail coach. While essentially 3D, the flow is directed from the bottom to the top of the carriage and comprises large to small eddies visualised by means of streamlines. The space and time distribution of the droplets and drops is computed using both an Eulerian model and a Lagrangian model. The results of the two modelling approaches are fully consistent and clearly illustrate the different behaviours of the drops, which fall down close to the infected passenger, and the droplets, which are carried along with the air flow and invade a large portion of the rail coach. This outcome is physically sound and demonstrates the relevance of CFD for simulating the transport and dispersion of droplets and drops with any diameter in enclosed ventilated spaces. As coughing produces drops and breathing produces droplets, both modes of transmission of the SARS-CoV-2 virus in human secretions have been accounted for in our 3D numerical study. Beyond the specific, practical application of the rail coach, this study offers a much broader scope by demonstrating the feasibility and usefulness of 3D numerical simulations based on CFD. As a matter of fact, the same computational approach that has been implemented in our study can be applied to a huge variety of ventilated indoor environments such as restaurants, performance halls, classrooms and open-plan offices in order to evaluate if their occupation could be critical with respect to the transmission of the SARS-CoV-2 virus or to other airborne respiratory infectious agents, thereby enabling relevant recommendations to be made.

Three-dimensional reconstruction by electron tomography for the application to ultrastructural analysis of SARS-CoV-2 particles.

SARS-CoV-2 is the cause of COVID-19. The three-dimensional morphology of viral particles existing and multiplying in infected cells has not been established by electron tomography, which is different from cryo-electron tomography using frozen samples. In this study, we establish the morphological structure of SARS-CoV-2 particles by three-dimensional reconstruction of images obtained by electron tomography and transmission electron microscopy of biological samples embedded in epoxy resin. The characteristic roots of spike structures were found to be arranged at the surface of a virion covered with an envelope. A high-electron-density structure that appears to be a nucleocapsid was observed inside the envelope of the virion on three-dimensional images reconstructed by electron tomography. The SARS-CoV-2 particles that budded in the vacuoles in the cytoplasm were morphologically identical to those found outside the cells, suggesting that mature and infectious SARS-CoV-2 particles were already produced in the vacuoles. Here, we show the three-dimensional morphological structure of SARS-CoV-2 particles reconstructed by electron tomography. To control infection, inhibition of viral release from vacuoles would be a new target in the development of prophylactic agents against SARS-CoV-2.

Semantic segmentation of COVID-19 lesions with a multiscale dilated convolutional network.

Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 lesions and the similarity between the lesions and normal tissues. This work aims to segment lesions of different scales and lesion boundaries correctly by utilizing multiscale and multilevel features. A novel multiscale dilated convolutional network (MSDC-Net) is proposed against the scale difference of lesions and the low contrast between lesions and normal tissues in CT images. In our MSDC-Net, we propose a multiscale feature capture block (MSFCB) to effectively capture multiscale features for better segmentation of lesions at different scales. Furthermore, a multilevel feature aggregate (MLFA) module is proposed to reduce the information loss in the downsampling process. Experiments on the publicly available COVID-19 CT Segmentation dataset demonstrate that the proposed MSDC-Net is superior to other existing methods in segmenting lesion boundaries and large, medium, and small lesions, and achieves the best results in Dice similarity coefficient, sensitivity and mean intersection-over-union (mIoU) scores of 82.4%, 81.1% and 78.2%, respectively. Compared with other methods, the proposed model has an average improvement of 10.6% and 11.8% on Dice and mIoU. Compared with the existing methods, our network achieves more accurate segmentation of lesions at various scales and lesion boundaries, which will facilitate further clinical analysis. In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.

Virus morphology: Insights from super-resolution fluorescence microscopy.

As epitomised by the COVID-19 pandemic, diseases caused by viruses are one of the greatest health and economic burdens to human society. Viruses are 'nanostructures', and their small size (typically less than 200 nm in diameter) can make it challenging to obtain images of their morphology and structure. Recent advances in fluorescence microscopy have given rise to super-resolution techniques, which have enabled the structure of viruses to be visualised directly at a resolution in the order of 20 nm. This mini-review discusses how recent state-of-the-art super-resolution imaging technologies are providing new nanoscale insights into virus structure.

3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography.

For the first time, we have used phase-contrast X-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopathological examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross-section of 3.5mm were scanned at a laboratory setup as well as at a parallel beam setup at a synchrotron radiation facility the synchrotron in a parallel beam configuration. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by high-resolution cone beam X-ray tomography and scanning electron microscopy. Furthermore, we implemented a fully automated analysis of the tissue structure in the form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis.