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42 results for “Image Acquisition”
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
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Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Reference Reflectance Transformation Imaging acquisitions for RTI stitching and acquisition optimization
<p>This dataset contains 1. RTI acquisitions a canvas painting and a metal print plate in parts, for development of RTI-stitching methods. 2. Dense RTI acquisitions of brushed metal and ruse coarse metal surfaces for development of methods for determining ideal light positions in a RTI acquisitions. </p>
Hyperspectral imager acquisitions from SMART Soils Test Bed, 2022-04-18
<p>Hyperpsectral imager data retrieved over the Lawrence Berkeley National Lab SMART Soils Test Bed on 2022-04-18 at three times (11:20, 12:34, 13:44). Radiance data (mW cm−2 μm−1 sr−1) subset to bands of interest over the Headwall Hyperspec Imager's 680-800nm range (680-682, 757-652, 769-772, and 778-780 nm) to retrieve RED, NIR, NDVI and SIF while minimizing file size. Data associated with Ruehr et al. 2023, 'Quantifying seasonal and diurnal cycles of solar-induced fluorescence with a novel hyperspectral imager,' submitted to Geophysical Research Letters in November 2023. Code for processing these data and descriptions of the files are available at https://github.com/sruehr/SIFretrieval.</p>
X-ray image reconstruction for continuous acquisitions with a generalized motion model: Data
<p>This dataset contains two experimentally measured X-ray scans, one reference scan and one scan in which the object translates while rotating. The reference scan consists of 3600 projection images, with one flat field and dark field image. The roto-translational scan consists of 360 projections, also with a flat field and dark field image. The acquisition files containing all relevant specifications of the scanner and the acquisition settings are also supplied.</p> <p>The code for reconstruction is available at <a href="https://github.com/BenHuyge/RACE">GitHub.</a></p>
NEMA image quality phantom acquisition on the Siemens mMR scanner
<p>NEMA image quality (IQ) phantom data acquired on the Siemens Biograph mMR PET/MR scanner. 60 minutes of PET data were acquired. The list mode acquisition and associated files required for reconstruction are provided.</p>
An evaluation of inexpensive methods for root image acquisition when using rhizotrons
<p>Dataset presenting the root data analysis performed in the paper:</p> <p><strong>An evaluation of inexpensive methods for root image acquisition when using rhizotrons</strong></p> <p><sup>1</sup>Awaz Mohamed*, <sup>1</sup>Yogan Monnier, <sup>1</sup>Zhun Mao, <sup>2,3</sup>Guillaume Lobet, <sup>4</sup>Jean-Luc Maeght, <sup>1</sup>Merlin Ramel, <sup>1</sup>Alexia Stokes</p> <p>The dataset contains:<br> Images examples_LaboratoryTest.7z: original images from laboratory rhizotron test including examples of images from camera, smartphone and scanners methods before and after image stitching (Test1)<br> Images examples in situ rhizotron.7z: original images of roots system from in situ rhizotron (Test2)<br> Day&night root growth by Camera.7z: original images of root growth monitored by time-lapse camera using in situ rhizotron (Test3).<br> Root_data.CSV.7z: Root data exported from SmartRoot and used for analysis in R<br> Root_Data_Analysis.R.7z: R scripts used for root data analysis</p>
Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia's Northwest Shelf; supplementary material
<p>This dataset comprises two supplementary materials. Supplementary Materials A includes seismic processing workflows conducted by industry on the seismic lines used in this study. The seismic processing workflows are not the property of the author but are publicly available on the NOPIMS and WAPIMS databases. Collating these workflows into supplementary materials provides a simple method for readers to access material important for this research paper. Supplementary Materials B is a collection of 2D seismic lines the author conducted stratigraphic horizon mapping on for this study as viewed in 3D. More details on this dataset can be found throughout the research paper "Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia’s Northwest Shelf".</p>
Fluorescence images acquisition on cultures of human-derived cardiomyocytes
<p><span>These data are used to evaluate the performance of the SiMulTox platform in terms of performance of live-cell fluorescence imaging.</span></p>
Datasets used for Automatic Acquisition of Non-Saturated Hyperspectral Images
<p>data-sets acquired for studying correlation between automatic exposure times and hyper-spectral images, with the aim of devising procedures for automatic acquisition of non-saturated hyper-spectral images</p>
Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training
<p>The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers.</p>
Adaptive CT Acquisition for Personalised Thoracic Imaging
ClinicalTrials.gov study NCT04070586. IPD Sharing: YES. Countries: 1. Publications: 1.
Determining the Validity of ThinkSono Guidance for Ultrasound Image Acquisition and Remote Detection
ClinicalTrials.gov study NCT06652568. IPD Sharing: NO. Countries: 1. Publications: 8.
The AstroPath Image Acquisition and Segmentation Workflow
<p>Multidimensional, spatially resolved analyses of cells from pathology slides are of great diagnostic and prognostic interest. New multispectral, multiplex immunofluorescence microscopy platforms have the potential to facilitate such analyses, and here, we further improve and standardize the image acquisition and cell classification workflow. Studies to date on this emerging technology have typically assessed ~10 operator-dependent high power fields (HPFs) per slide, which represents a fraction of the tissue available for study. Standard cell segmentation and classification algorithms often oversegment larger cells, when they are segmented at the same time as smaller cells. Here we describe our AstroPath imaging platform, which addresses each of these considerations. In our study, slides from formalin-fixed paraffin embedded tissue specimens were stained with an optimized 6-plex multiplex immunofluorescence (mIF) assay. The slides were then scanned at 35 unique wavelengths using a multispectral microscope (Vectra 3.0 or Vectra Polaris) with 20% overlap of HPFs in an operator-independent fashion. An average of 1300 HPFs per slide was required to image the entire tissue, and each microscope scanned between 2 to 3 slides per day with this approach. After the images were captured and organized, overlaps were used to measure, quantify and correct systematics in the imagery (see Eminizer abstract). The central parts of the images were used to create a set of seamless “primary” tiles, similar to the strategy of the Sloan Digital Sky Survey, for a statistically fair pixel coverage of the whole tissue area (see Roskes abstract). Images were then linearly unmixed from the 35 wavelengths to 8 component layers (DAPI, tissue auto-fluorescence, and the 6 added fluorescent dyes) using inForm Cell Analysis©. We then employed a bespoke method for ‘multi-pass’ classification of cells wherein each marker was segmented and classified separately from the other markers, then merged into a single plane using a unique set of rules and predefined cell hierarchy. We showed that our segmentation and classification method reduced error in over-counting larger cells, e.g. tumor cells, by 25% and increased the specificity and sensitivity in each classification algorithm. Due to the amount of data, each algorithm was run automatically through one of 20 virtual machines housed on a set of servers in the Physics and Astronomy Department. Following the methodology developed during the SDSS project, image data was stored in a well-defined file system structure that facilitated further automatic processing and ingestion into a SQL Server database. Raw data for each slide was 200-300 GBs, which is on par with a full scale (30x) human genome. In summary, we have developed a unique facility and workflow that generates whole slide multispectral imagery with high-fidelity, single cell resolution. Our facility houses five multispectral microscopes (2 Vectra 3.0 and 3 Vectra Polaris) allowing us to collect a petabyte of raw data per year, on scale of the largest sky survey.</p>
The Effectiveness of 4D Image Acquisition and Post-processing With Vios Works
ClinicalTrials.gov study NCT03128268. IPD Sharing: NO. Countries: 1. Publications: 7.
Evaluation of MRI Sequences for Ultra-rapid Acquisition of Bile Ducts Images
ClinicalTrials.gov study NCT03852836. IPD Sharing: NO. Countries: 1. Publications: 3.
Assessment of Physiological Parameters Measurements (Heart Rate, Respiratory Rate, and Oxygen Saturation) by Standard Acquisition System Compared Remote Photoplethysmography Imaging System l
ClinicalTrials.gov study NCT04660318. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PAAQ-Heart : Beating Embryonic Zebrafish Heart Imaged by Paired Alternating Acquisitions (PAAQ) Microscopy
<p><strong>Description</strong></p><p>This dataset contains images of the beating heart of live embryonic zebrafish acquired using Paired Alternating AcQuisitions (PAAQ) microscopy.</p><p>The dataset was collected to illustrate a multichannel video reconstruction method, which virtually increases the framerate of a repeating process (e.g., cardiac heartbeat) as compared to what would be achievable through direct imaging with a single-channel fluorescence setup and a low-framerate camera.</p><p>The imaged embryos were obtained by crossing transgenic Tg(<i>fli1a:GFP</i>)<i>y1Tg</i> and Tg(<i>myl7:mRFP</i>)<i>ko08Tg</i> zebrafish and selecting homozygous embryos. The embryos were were imaged at 4dpf on an implementation of the OpenSPIM microscope equiped with and the illumination controller available as part of the CBI-MMTools microscopy controltools package for micromanager (https://github.com/idiap/CBI-MMTools) to generate alternating illumination patterns in even and odd frames. The odd frames correspond to illumination by a brightfield with intensity that increases as a ramp over the duration of the frame exposure (reference modality) and even frames are either the result of laser illumination (fluorescence imaging) or a short brightfield illumination burst.</p><p> </p><p><strong>Reference</strong></p><p>If you use this dataset, please cite the following publication:</p><p><i>Marelli, F., Ernst, A., Mercader, N., Liebling, M. (2023). PAAQ: Paired Alternating AcQuisitions for Virtual High Frame Rate Multichannel Cardiac Fluorescence Microscopy. Biological Imaging, 3, E20.</i><br><a href="https://doi.org/10.1017/S2633903X23000223 ">10.1017/S2633903X23000223</a></p>
Supplementary material 1 from: Thanayutsiri T, Charoenying T, Patrojanasophon P, Pamornpathomkul B, Opanasopit P, Ngawhirunpat T, Rojanarata T (2023) Facile, sensitive and reagent-saving smartphone-based digital image colorimetric assay of captopril tablets enabled by long-pathlength RGB acquisition. Pharmacia 70(4): 1511-1519. https://doi.org/10.3897/pharmacia.70.e114927
Supplementary data
Acquisition of Breast Mammography Images
ClinicalTrials.gov study NCT02156258. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.