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45 results for “Diffuse Optics”
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
<p>We demonstrate a novel realization of Interstitial fiber, broadband, Time Domain Diffuse Optical Spectroscopy (TD-DOS) in Null Source-Detector separation (NSDS) approach without temporal gating, by using a Superconducting Nanowire single photon detector (SNSPD) for acquisition. As per the MEDPHOT protocol, we test experimentally, the absorption linearity of the system on tissue-equivalent liquid phantoms, and demonstrate the scattering-independent retrieval of the absorption spectrum of water using Intralipid phantoms in the wavelength range of 600-1100 nm.</p> <p>This work has been published in the Journal of Biomedical Optics - https://doi.org/10.1117/1.JBO.28.12.121202. Here, we present the dataset containing the acquired data pertaining to the aforementioned publication, including a brief overview, the tools to read it and the analysis corresponding to the figures in the article.</p>
Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
<p>Included are the data presented in the publication entitled: <em>Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice</em> accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes:</p> <p>1. Data (duplicated in .xlsx and .mat):</p> <p> </p> <p>1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4)</p> <p> </p> <p>1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations</p> <p> —cumu_sg- cumulative signal (%)</p> <p> — depth (mm)</p> <p> —standard deviation on depth where signal is cumulated</p> <p> —ddet (mm)- radial distance between source and detection point </p> <p> — b (m^-1)-scattering coefficient</p> <p> </p> <p>1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions </p> <p> </p> <p>—vf (-)- microspheres volume fraction (in water)</p> <p> —a_theo (m^-1) - theoretical value of the absorption coefficient</p> <p> — mean_error_a(%) - error between theoretical value and measured value</p> <p> —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_a_y (%) -standard deviation on error_a </p> <p> —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient</p> <p> —mean_error_rb(%) - error between theoretical value and measured value</p> <p> —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_rb_y (%)) -standard deviation on error_rb </p> <p> —gamma_theo (-) - theoretical value of gamma</p> <p> —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_gamma (%) - standard deviation on error_gamma</p> <p> </p> <p>-1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —T (celsius) - ice temperature</p> <p> —S_si (ppt) - ice bulk salinity</p> <p> —depth (cm)</p> <p> </p> <p>1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —Rmes (-) - spatially resolved diffuse Reflectance</p> <p> —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes</p> <p> —dmes (mm) - distance between source and detecting fibre (named rho in the paper)</p> <p> —depth (cm)</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4</p> <p> </p> <p> —infferedrb (m^-1) - reduced scattering coefficient</p> <p> —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes</p> <p> —cr1 (binary)— criteria determining if the measurement is kept or not</p> <p> —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice.</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>2. Code (written in .m with MATLAB_R2018b ®) :</p> <p> </p> <p>2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice</p> <p> </p> <p>— Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes)</p> <p>—importfiledata.m-subfunction to import data from .csv </p> <p>—importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted)</p> <p>—interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion</p> <p>—calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference</p> <p>—site1_c20-picture of the ice core taken at site 1</p> <p>—site4_c20-picture of the ice core taken at site 4</p> <p>—may8th+othertests_fixed.csv-raw data from may 8 (site1)</p> <p>—may9day3.csv-raw data from may 9 (site4)</p> <p> </p> <p> </p>
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>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Laboratory validation of a smartphone-based sensor for diffuse optical volume properties
<p>This data set contains raw image data for laboratory validation of a diffuse-optical, smartphone-based sensor. The measurements were taken using scattering phantoms with known scattering and absorption coefficient. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the three scattering phantoms (One, Two, Three) or spatial calibration data using a 9mm x 9mm calibration pattern (calib). The raw images are located in the folder ./calib. Matlab code is contained in the filder ./matlab. It can be run on Matlab R2021b.</p> <p>For analyzing the raw data set, use "CameraBatchCalib.m". It wraps around the function "CameraAnalysisCalib.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfG.m". The resulting data is plotted for comparison with the nominal attenuation length of the scattering phantoms.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset and code. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022).</p> <p>The underlying fit function is based on the calculations from:</p> <p>Markus Allgaier and Smith, Diffuse optics for glaciology, Opt. Express 29, 18845–18864 (2021)</p> <p> </p>
Coherent enhancement of optical remission in diffusive media
<p>Experimental data sets associated with "Coherent enhancement of optical remission in diffusive media" (https://doi.org/10.1073/pnas.2207089119).</p>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
Open the record for dataset details and reuse information.
Involvement of the optic nerve in mutated CSF1R-induced hereditary diffuse leukoencephalopathy with axonal spheroids
<p><strong>Figure 1:</strong> Family pedigree. The arrow indicates the proband (present patient). Her mother developed a motor disorder at 40 years of age and died at 60 years of age. Her grandparents, father, brothers, sisters, and daughters were not affected.</p> <p><strong>Figure 2: Brain MRI, DWI, DTI, and MRS images. T2/Flair showed multifocal periventricular white matter lesions (A, B, and C), without enhancement (D). DWI shows high-signal intensities in periventricular white matters and corpus callosum (E, F). DTI shows decreased numbers of corpus callosum fibers, while subcortical arcuate fibers are spared (G). MRS shows increased Cho levels, while NAA levels are decreased in the white matter lesions (H, I).</strong></p> <p><strong>Figure 3: </strong>Optic nerves on MRI, showing that bilateral optic nerves are injured (red arrows). </p> <p><strong>Figure 4: </strong>OCT shows that the right peripapillary retinal nerve fiber layer (pRNFL) is atrophic in the temporal quadrant, and the left pRNFL is thinning in the temporal superior quadrants. Green represents pRNFL thickness, which is within normal limits; yellow represents pRNFL thickness, which is below borderline; red represents pRNFL thickness, which is below normal limits.</p> <p><strong>Figure 5:</strong> VEP shows reduced bilateral P100 amplitudes, although P100 latencies are normal in both eyes.</p> <p><strong>Figure 6:</strong> Visual fields in the right eye are partially missing in the upper right, lower right, and lower left quadrants, especially in the lower right quadrant. Visual fields in the left eye are partially missing in the four quadrants, especially in the upper left and lower right quadrants.</p> <p><strong>Figure 7:</strong> Gene analysis of <em>CSF1R</em>. The sequencing result from exon 18 of <em>CSF1R</em> (NM_005211.3) indicates a heterozygous c.2345 G>A (p.782Arg>His) substitution in the patient.</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>
Dataset of "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy"
<p>Dataset for the article entitled "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy" </p> <p> </p> <p>Abstract:</p> <p> </p> <p>Thermal Therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions.<br>In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues, based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C.<br>We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and proteins secondary structure.<br>After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin.<br>This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.</p> <p> </p> <p> </p> <p>Authors:</p> <p>ALESSANDRO BOSSI , LEONARDO BIANCHI , PAOLA SACCOMANDI , AND ANTONIO PIFFERI<br>Politecnico di Milano</p> <p> </p> <p> </p> <p><a href="https://opg.optica.org/boe/fulltext.cfm?uri=boe-15-4-2481&id=548108" target="_blank" rel="noopener">Link to the article</a></p>
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber's Hereditary Optic Neuropathy. RAW DATA
<p>Raw data used to prepare Figures and Tables in article "Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber’s Hereditary Optic Neuropathy", accepted for publication at International Journal of Environmental Research and Public Health, 27 May 2022.</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>
Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: Breast tissue composition changes and collagen discriminative potential
<p>This dataset serves as a comprehensive resource containing the raw data, analyses, and various tools utilized to produce the findings that are presented in our paper titled ‘<strong>Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: breast tissue composition changes and collagen discriminative potential</strong>’. </p> <p>The aim of this clinical study is to test broad spectral range (635-1060 nm) time domain diffuse optical spectroscopy to monitor the response of breast cancer patients to neoadjuvant chemotherapy. Preliminary results from patients on changes in hemoglobin, water, lipids, collagen concentrations, and scattering parameters due to the therapy are presented. In these initial results, we hypothesize that collagen could be a possible biomarker to distinguish complete and partial responders.</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>
Involvement of the optic nerve in mutated CSF1R-induced hereditary diffuse leukoencephalopathy with axonal spheroids
<p>Figure legends</p> <p>Figure 1: Family pedigree. The arrow indicates the proband (present patient). Her mother developed a motor disorder at 40 years of age and died at 60 years of age. Her grandparents, father, brothers, sisters, and daughters were not affected.</p> <p> </p> <p>Figure 2: Brain MRI, DWI, DTI, and MRS images. T2/Flair showed multifocal periventricular white matter lesions (A, B, and C), without enhancement (D). DWI shows high-signal intensities in periventricular white matters and corpus callosum (E, F). DTI shows decreased numbers of corpus callosum fibers, while subcortical arcuate fibers are spared (G). MRS shows increased Cho levels, while NAA levels are decreased in the white matter lesions (H, I).</p> <p> </p> <p>Figure 3: Optic nerves on MRI, showing that bilateral optic nerves are injured (red arrows).</p> <p> </p> <p>Figure 4: OCT shows that the right peripapillary retinal nerve fiber layer (pRNFL) is atrophic in the temporal quadrant, and the left pRNFL is thinning in the temporal superior quadrants. Green represents pRNFL thickness, which is within normal limits; yellow represents pRNFL thickness, which is below borderline; red represents pRNFL thickness, which is below normal limits.</p> <p> </p> <p>Figure 5: VEP shows reduced bilateral P100 amplitudes, although P100 latencies are normal in both eyes.</p> <p> </p> <p>Figure 6: Visual fields in the right eye are partially missing in the upper right, lower right, and lower left quadrants, especially in the lower right quadrant. Visual fields in the left eye are partially missing in the four quadrants, especially in the upper left and lower right quadrants.</p> <p> </p> <p>Figure 7: Gene analysis of CSF1R. The sequencing result from exon 18 of CSF1R (NM_005211.3) indicates a heterozygous c.2345 G>A (p.782Arg>His) substitution in the patient.</p>
DATA SET: Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies
<p>This repository contains the data sets of the article:</p> <p>P. Fernández Esteberena et al. (2024). Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies. <em>Biomedical Optics Express</em>. <a href="https://doi.org/10.1364/BOE.538141">https://doi.org/10.1364/BOE.538141</a></p>
Data for "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble"
<p>The files uploaded here include the data shown in Figures 3.c), 4.a) and 4.b) of the article "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble", that can be found in: arXiv:2409.11117 </p> <p>Four datasets are included: </p> <p>df_diffusion.csv --> Figure 3.c</p> <p>df_vtransport.csv --> Figure 4.a </p> <p>df_ttransport.csv --> Inset figure 4.a </p> <p>df_decay.csv --> Figure 4.b</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.