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8 results for “diffuse optical tomography”
Diffuse Optical Tomography and Fluorescence Simulation
<p>Diffusion of a source of light (Dirac) in a turbid medium. The object owns two inclusions, one more absorbant and one more diffusive than the background. These inclusions can be seen as tumours that have different optical and fluorescence properties compared to the "homogeneous" background.</p> <p>This simulation shows the forward problem solutions for choosen optical and fluorescence parameters and computed with FEEL++, a C++ library for Generalized Garlerkin methods (FEM, HP-FEM, ...).</p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<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. 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 5-10 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.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, 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.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</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>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</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 OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </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>
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>
Quantitative Assessment and Characterization of Microvascular Function Using Diffuse Optical Tomography
ClinicalTrials.gov study NCT03411213. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Diffuse Optical Tomography (DOT) for Monitoring Response to Neoadjuvant (Preoperative) Chemotherapy in Breast Cancer
ClinicalTrials.gov study NCT01394315. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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