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448 results for “Optical tomography”
Plasmonic Copper Sulfide Nanoparticles Enable Dark Contrast in Optical Coherence Tomography
<p>Dataset of https://onlinelibrary.wiley.com/doi/10.1002/adhm.201901627</p>
Dataset provided with "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction"
<p>Dataset and scripts used in "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction".</p>
Optical coherence tomography of the macular ganglion cell layer in children with neurofibromatosis type 1 is a useful tool in the assessment for optic pathway gliomas
<p><span>To investigate whether the ganglion cell layer assessed by OCT is a reliable measure to identify and detect relapses of symptomatic OPGs in children with NF1.</span></p>
Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion</em>”</strong> in Optics Express (doi.org/10.1364/OE.521702)<em>. </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage.</p> <p>We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>10050, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>CS50-28, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Mix, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated mixed sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Dark, 10 us.mat</p> </td> <td> <p>Background interference intensity from a custom setup.</p> </td> <td> <p>Na=2048, Nb=5, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Concentrated 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated mixed sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute mixed sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data for the Thorlabs OCT measurements.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis functions.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Custom_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the custom setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_dilute.py</p> </td> <td> <p>The script is for running analysis of raw dilute measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Diffuse Optical Tomography Dataset - Unstructured Scans
<p>This repository houses a simulated dataset designed for Frequency-Domain Diffuse Optical Tomography (FD-DOT) experiments. It includes a total of 60,000 examples, each comprised of a target volume representing 3D absorption and reduced scattering properties - randomized within a biologically realistic range for human breast tissue - plus amplitude and phase components of corresponding frequency-domain reflectance measurements, for a randomly selected subset of high-density source detector positions. The dataset encompasses raw data, preprocessed data, mesh information, and supplementary metadata.</p>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography</em>”</strong> in Optics Express (doi.org/10.1364/OE.474279<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 10 minutes and 2D flow profile analysis may take up to one hour.</p> <p>For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, <em>Q</em>. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement.</p> <p>For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, <em>Q</em>. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Usability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 15-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 22-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 08-07-2022.zip</p> </td> <td> <p>2D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>All measurements</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>Processing.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines particle size from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Shape.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines axial beam shape from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Doppler Angle.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow Profile.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Number Density.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines particle number density raw OCT spectra.</p> </td> </tr> <tr> <td> <p>2D Flow Profile.py</p> </td> <td> <p>2D measurements</p> </td> <td> <p>This script determines 2D flow profiles from raw OCT spectra.</p> </td> </tr> </tbody> </table> <p> </p>
Dataset for Unrolled-DOT: An Interpretable Deep Network for Diffuse Optical Tomography
<p>Official dataset repository for Unrolled-DOT: An Interpretable Deep Network for Diffuse Optical Tomography. The repository contains both the experimental dataset (allTrainingDat_30-Sep-2021.mat) as well as data that is a dependency for running our code (5_29_21_src-det_10x10_scene_4cm.zip).</p>
SPIRIT Checklist & Model Consent for 'Predicting Acute and Post-Recovery Outcomes in Cerebral Malaria and Other Comas by Optical Coherence Tomography (OCT in CM) – A protocol for an observational cohort study of Malawian children'
<p>This dataset contains the SPIRIT checklist (adapted to a observational trial) and model consent forms for the OCT in CM study protocol. The protocol will be submitted as a paper to Wellcome Open Research.</p>
Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source
<p>This repository contains the code and data underlying the publication "<em>Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source</em>" in Biomedical Optics Express 14, 3532-3554 (2023) (doi.org/10.1364/BOE.487345).</p> <p>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author. </p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes all the Matlab and Python scripts in this depository. Table 2 describes the datasets. The input datasets are the phase corrected datasets, as the raw data is large in size and phase correction using a coverslip as reference is rather straightforward. Processed datasets are also added to the repository to allow for running only a limited number of scripts, or to obtain for example the aberration corrected data without the need to use python. Note that the simulation input data (<em>input_simulations_pointscatters_SLDshape_98zf_noise75.mat</em>) is generated with random noise, so if this is overwritten de results may slightly vary. Also the aberration correction is done with random apertures, so the processed aberration corrected data (<em>exp_pointscat_image_MIAA_ISAM_CAO.mat</em> and <em>exp_leaf_image_MIAA_ISAM_CAO.mat</em>) will also slightly change if the aberration correction script is run anew. The current processed datasets are used as basis for the figures in the publication. For details on the implementation we refer to the publication.</p> <table> <caption>Table 1: The Matlab and Python scripts with their description</caption> <tbody> <tr> <td><strong>Script name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><em>MIAA_ISAM_processing.m</em></td> <td>This scripts performs the DFT, RFIAA and MIAA processing of the phase-corrected data that can be loaded from the datasets. Afterwards it also applies ISAM on the DFT and MIAA data and plots the results in a figure (via the scripts <em>plot_figure3, plot_figure5</em> and <em>plot_simulationdatafigure</em>).</td> </tr> <tr> <td><em>resolution_analysis_figure4.m</em></td> <td>This figure loads the data from the point scatterers (absolute amplitude data), seeks the point scatterrers and fits them to obtain the resolution data. Finally it plots figure 4 of the publication.</td> </tr> <tr> <td><em>fiaa_oct_c1.m, oct_iaa_c1.m, rec_fiaa_oct_c1.m, rfiaa_oct_c1.m</em> </td> <td>These four functions are used to apply fast IAA and MIAA. See <em>script MIAA_ISAM_processing.m</em> for their usage.</td> </tr> <tr> <td><em>viridis.m, morgenstemning.m</em></td> <td>These scripts define the colormaps for the figures.</td> </tr> <tr> <td><em>plot_figure3.m, plot_figure5.m, plot_simulationdatafigure.m</em></td> <td>These scripts are used to plot the figures 3 and 5 and a figure with simulation data. These scripts are executed at the end of script <em>MIAA_ISAM_processing.m.</em></td> </tr> <tr> <td>Python script: <em>computational_adaptive_optics_script.py</em></td> <td>Python script that applied computational adaptive optics to obtain the data for figure 6 of the manuscript.</td> </tr> <tr> <td>Python script: <em>zernike_functions2.py</em></td> <td>Python script that gives the values and carthesian derrivatives of the Zernike polynomials.</td> </tr> <tr> <td><em>figure6_ComputationalAdaptiveOptics.m</em></td> <td>Script that loads the CAO data that was saved in Python, analyzes the resolution, and plots figure 6.</td> </tr> <tr> <td>Python script: <em>OCTsimulations_3D_script2.py</em></td> <td>Python script simulates OCT data, adds noise and saves it as .mat file for use in the matlab script above.</td> </tr> <tr> <td>Python script: <em>OCTsimulations2.py</em></td> <td>Module that contains a python class that can be used to simulate 3D OCT datasets based on a Gaussian beam.</td> </tr> <tr> <td>Matlab toolbox DIPimage 2.9.zip</td> <td>Dipimage is used in the scripts. The toolbox can be downloaded online or this zip can be used.</td> </tr> </tbody> </table> <table> <caption>The datasets in this Zenodo repository</caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>input_leafdisc_phasecorrected.mat</td> <td>Phase corrected input image of the leaf disc (used in figure 5).</td> </tr> <tr> <td>input_TiO2gelatin_004_phasecorrected.mat</td> <td>Phase corrected input image of the TiO2 in gelatin sample.</td> </tr> <tr> <td>input_simulations_pointscatters_SLDshape_98zf_noise75</td> <td>Input simulation data that, once processed, is used in figure 4.</td> </tr> <tr> <td> <p>exp_pointscat_image_DFT.mat</p> <p>exp_pointscat_image_DFT_ISAM.mat</p> <p>exp_pointscat_image_RFIAA.mat</p> <p>exp_pointscat_image_MIAA_ISAM.mat</p> <p>exp_pointscat_image_MIAA_ISAM_CAO.mat</p> </td> <td>Processed experimental amplitude data for the TiO2 point scattering sample with respectively DFT, DFT+ISAM, RFIAA, MIAA+ISAM and MIAA+ISAM+CAO. These datasets are used for fitting in figure 4 (except for CAO), and MIAA_ISAM and MIAA_ISAM_CAO are used for figure 6.</td> </tr> <tr> <td> <p>simu_pointscat_image_DFT.mat</p> <p>simu_pointscat_image_RFIAA.mat</p> <p>simu_pointscat_image_DFT_ISAM.mat</p> <p>simu_pointscat_image_MIAA_ISAM.mat</p> </td> <td>Processed amplitude data from the simulation dataset, which is used in the script for figure 4 for the resolution analysis.</td> </tr> <tr> <td> <p>exp_leaf_image_MIAA_ISAM.mat</p> <p>exp_leaf_image_MIAA_ISAM_CAO.mat</p> </td> <td>Processed amplitude data from the leaf sample, with and without aberration correction which is used to produce figure 6.</td> </tr> <tr> <td> <p>exp_leaf_zernike_coefficients_CAO_normal_wmaf.mat</p> <p>exp_pointscat_zernike_coefficients_CAO_normal_wmaf.mat</p> </td> <td>Estimated Zernike coefficients and the weighted moving average of them that is used for the computational aberration correction. Some of this data is plotted in Figure 6 of the manuscript.</td> </tr> <tr> <td>input_zernike_modes.mat</td> <td>The reference Zernike modes corresponding to the data that is loaded to give the modes the proper name.</td> </tr> <tr> <td> <p>exp_pointscat_MIAA_ISAM_complex.mat</p> <p>exp_leaf_MIAA_ISAM_complex</p> </td> <td>Complex MIAA+ISAM processed data that is used as input for the computational aberration correction. </td> </tr> </tbody> </table> <p> </p>
Evaluation of HealinG of Polymer-Free Biomlimus A9-Coated Stent by Optical Coherence Tomography (EGO-BIOFREEDOM)
ClinicalTrials.gov study NCT01760876. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Optical Coherence Tomography Angiography Evaluation of Ocular Changes in Patients With Carotid Artery Stenosis
ClinicalTrials.gov study NCT04326842. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
In Vivo Assessment of the Tooth-Resin Composite Interface Using Optical Coherence Tomography
ClinicalTrials.gov study NCT05145322. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Sutureless Glueless Technique Versus Interrupted Suturing for Conjunctival Autograft Fixation After Primary Pterygium Excision: Anterior Segment Optical Coherence Tomography (ASOCT) Study
ClinicalTrials.gov study NCT07307820. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Imaging of Peripheral Arteries by Optical Coherence Tomography and Intravascular Ultrasound Imaging
ClinicalTrials.gov study NCT03480685. IPD Sharing: NO. Countries: 1. Publications: 1.
Lithotripsy Versus Balloon Angioplasty for Optimal Treatment of CAlcified Lesions With and Without Optical Coherence Tomography evaluatION
ClinicalTrials.gov study NCT07388030. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Quantifying changes on optical coherence tomography in eyes receiving treatment for neovascular age-related macular degeneration
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Quantifying the influence of optical coherence tomography beam tilt in the normal adult mouse retina
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Non-invasive investigation of the morphology and optical properties of the upside-down jellyfish Cassiopea with optical coherence tomography
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Data from: Is retina affected in Huntington's disease? Is optical coherence tomography a good biomarker?
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.