Visualization and quantification of the stromal-vascular compartment in fetal or adult mouse bones: From sampling to high-resolution 3D image analysis
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ABSTRACT:
SUBMITTER: Peredo N
PROVIDER: S-EPMC9697533 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
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| S-BIAD287 | bioimages
Project description:PurposeTo explore a fetal 3D cardiovascular cine acquisition using a radial image acquisition and compressed-sensing reconstruction and compare image quality and scan time with conventional multislice 2D imaging.MethodsVolumetric fetal cardiac data were acquired in 26 volunteers using a radial 3D balanced SSFP pulse sequence. Cardiac gating was performed using a Doppler ultrasound device. Images were reconstructed using a parallel-imaging and compressed-sensing algorithm. Multiplanar reformatting to standard cardiac views was performed before image analysis. Clinical 2D images were used for comparison. Qualitative and quantitative image evaluation were performed by two experienced observers (scale: 1-4). Volumes, mass, and function were assessed.ResultsAverage scan time for the 3D imaging was 6 min, including one localizer. A 2D imaging stack covering the entire heart including localizer sequences took at least 6.5 min, depending on planning complexity. The 3D acquisition was successful in 7 of 26 subjects (27%). Overall image contrast and perceived resolution were lower in the 3D images. Nonetheless, the 3D images had, on average, a moderate cardiac diagnostic quality (median [range]: 3 [1-4]). Standard clinical 2D acquisitions had a high cardiac diagnostic quality (median [range]: 4 [3, 4]). Cardiac measurements were not different between 2D and 3D images (all p > 0.16).ConclusionThe presented free-breathing whole-heart fetal 3D radial cine MRI acquisition and reconstruction method enables retrospective visualization of all cardiac views while keeping examination times short. This proof-of-concept work produced images with diagnostic quality, while at the same time reducing the planning complexity to a single localizer.
| S-EPMC10087603 | biostudies-literature
Project description:MotivationQuantifying lateral resolution as a function of depth is important in the design of 3D microscopy experiments. However, for many specimens, resolution is non-uniform within the same optical plane because of factors such as tissue variability and differential light scattering. This precludes application of a simple resolution metric to the image as a whole. In such cases, it can be desirable to analyse resolution only within specific, well-defined features.ResultsAn algorithm and software are presented to characterize resolution as a function of depth in features of arbitrary shape in 3D samples. The tool can be used to achieve an objective comparison between different preparation methods, imaging parameters, and optical systems. It can also inform the design of experiments requiring resolution of structures at a specific scale. The method is demonstrated by quantifying the improvement in resolution of two-photon microscopy over confocal in the central brain of Drosophila melanogaster. Measurement of image quality increases by tuning a single parameter, laser power, is also shown. An ImageJ plugin implementation is provided for ease of use via a simple Graphical User Interface, with outputs in table, graph, and colourmap formats.Availability and implementationSoftware and source code are available at https://www.imperial.ac.uk/rowlands-lab/resources/.
| S-EPMC10749749 | biostudies-literature
Project description:MotivationQuantitative descriptions of multi-cellular structures from optical microscopy imaging are prime to understand the variety of three-dimensional (3D) shapes in living organisms. Experimental models of vertebrates, invertebrates and plants, such as zebrafish, killifish, Drosophila or Marchantia, mainly comprise multilayer tissues, and even if microscopes can reach the needed depth, their geometry hinders the selection and subsequent analysis of the optical volumes of interest. Computational tools to "peel" tissues by removing specific layers and reducing 3D volume into planar images, can critically improve visualization and analysis.ResultsWe developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks. The plugin implements spherical and spline surface projections. We applied VolumePeeler to perform peeling in 3D images of spherical embryos, as well as non-spherical tissue layers. The produced images improve the 3D volume visualization and enable analysis and quantification of geometrically challenging microscopy datasets.AvailabilityImageJ/FIJI software, source code, examples, and tutorials are openly available in https://cimt.uchile.cl/mcerda.
| S-EPMC10337153 | biostudies-literature
Project description:BackgroundRice quality research attracts attention worldwide. Rice chalkiness is one of the key indexes determining rice kernel quality. The traditional rice chalkiness measurement methods only use milled rice as materials and are mainly based on naked-eye observation or area-based two-dimensional (2D) image analysis and the results could not represent the three-dimensional (3D) characteristics of chalkiness in the rice kernel. These methods are neither in vivo thus are unable to analyze living rice seeds for high throughput screening of rice chalkiness phenotype.ResultsHere, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT). This approach not only develops a novel volume-based method to measure the 3D rice chalkiness index, but also provides a high throughput solution for rice chalkiness phenotype analysis by using living rice seeds.ConclusionsOur method could be a new powerful tool for rice chalkiness measurement, especially for high throughput chalkiness phenotype screening using living rice seeds. This method could be used in chalkiness phenotype identification and screening, and would greatly promote the basic research in rice chalkiness regulation as well as the quality evaluation in rice production practice.
| S-EPMC7494727 | biostudies-literature
Project description:Long-term accumulation in the soils of ubiquitous organic pollutants such as many polycyclic aromatic hydrocarbons (PAHs) depends on deposition from the atmosphere, revolatilization, leaching, and degradation processes such as photolysis and biodegradation. Quantifying the phase distribution and fluxes of these compounds across environmental compartments is thus crucial to understand the long-term contaminant fate. The gas-phase exchange between soil and atmosphere follows chemical fugacity gradients that can be approximated by gas-phase concentrations, yet which are difficult to measure directly. Thus, passive sampling, measured sorption isotherms, or empirical relationships to estimate sorption distribution have been combined in this study to determine aqueous (or gas) phase concentrations from measured bulk concentrations in soil solids. All these methods have their strengths and weaknesses but agree within 1 order of magnitude except for ex situ passive samplers employed in soil slurries, which estimated much lower concentrations in soil water and gas likely due to experimental artifacts. In field measurements, PAH concentrations determined in the atmosphere show a pronounced seasonality with some revolatilization during summer and gaseous deposition during winter, but overall dry deposition dominates annual mean fluxes. The characteristic patterns of PAHs in the different phases (gas phase, atmospheric passive samplers, bulk deposition, and soil solids) confirm the expected compound-specific distribution pattern and behavior. Since revolatilization fluxes in summer are only minor and wet and dry deposition is ongoing, our results clearly show that the PAH loads in topsoils will continue to increase.
| S-EPMC10125305 | biostudies-literature
Project description:To improve the clinical translation of anti-cancer nanomedicines, it is necessary to begin building specific insights into the broad concept of the Enhanced Permeability and Retention (EPR) effect, using detailed investigations of the accumulation, distribution and retention of nanomedicines in solid tumors. Nanomedicine accumulation in preclinical tumors has been extensively studied; however, treatment efficacy will be heavily influenced by both the quantity of drug-loaded nanomedicines reaching the tumor as well as their spatial distribution throughout the tumor. It remains a challenge to image the heterogeneity of nanomedicine distribution in 3 dimensions within solid tumors with a high degree of spatial resolution using standard imaging approaches. Methods: To achieve this, an ex vivo micro computed tomography (µCT) imaging approach was developed to visualize the intratumoral distribution of contrast agent-loaded PEGylated liposomes. Using this semi-quantitative method, whole 3-dimensional (3D) tumor liposome distribution was determined with 17 µm resolution in a phenotypically diverse panel of four preclinical xenograft and patient-derived explant (PDX) tumor models. Results: High-resolution ex vivo μCT imaging revealed striking differences in liposome distribution within tumors in four models with different vascular patterns and densities, stromal contents, and microenvironment morphologies. Following intravenous dosing, the model with the highest density of pericyte-supported vessels showed the greatest liposome accumulation, while the model with vessels present in regions of high α-smooth muscle actin (αSMA) content presented with a large proportion of the liposomes at depths beyond the tumor periphery. The two models with an unsupported vascular network demonstrated a more restricted pattern of liposome distribution. Conclusion: Taken together, vessel distribution and support (the latter indicative of functionality) appear to be key factors determining the accumulation and distribution pattern of liposomes in tumors. Our findings demonstrate that high-resolution 3D visualization of nanomedicine distribution is a useful tool for preclinical nanomedicine research, providing valuable insights into the influence of the tumor vasculature and microenvironment on nanomedicine localization.
| S-EPMC6929971 | biostudies-literature
Project description:Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility.
| S-EPMC4823793 | biostudies-literature
Project description:MotivationLive cell imaging plays a pivotal role in understanding cell growth. Yet, there is a lack of visualization alternatives for quick qualitative characterization of colonies.ResultsSeeVis is a Python workflow for automated and qualitative visualization of time-lapse microscopy data. It automatically pre-processes the movie frames, finds particles, traces their trajectories and visualizes them in a space-time cube offering three different color mappings to highlight different features. It supports the user in developing a mental model for the data. SeeVis completes these steps in 1.15 s/frame and creates a visualization with a selected color mapping.Availability and implementationhttps://github.com/ghattab/seevis/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6513157 | biostudies-literature
Project description:Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models either cannot scale to high-resolution or are prone to patchy artifacts. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantages: First, the memory demand for training on high-resolution images is amortized among sub-volumes. Furthermore, anchoring the high-resolution sub-volumes to a single low-resolution image ensures anatomical consistency between sub-volumes. During inference, our model can directly generate full high-resolution images. We also incorporate an encoder with a similar hierarchical structure into the model to extract features from the images. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation. We also demonstrate clinical applications of the proposed model in data augmentation and clinical-relevant feature extraction.
| S-EPMC9413516 | biostudies-literature