Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics

Purpose: Development and validation of an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera which provides simultaneously high definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity in order to calculate the absolute retinal blood flow.\newline Methods: This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes$^\copyright$,Orsay, France) outside its optical wavefront correction path. A 4 seconds recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides the vessel diameter close to the probing beam and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. Combination of those values gives an average of the absolute retinal blood flow. An in vitro experiment consisting of latex microspheres flowing in water through a glass-capillary to simulate a blood vessel and in vivo measurements on six healthy humans were done to assess the device.\newline Results: In the in vitro experiment, the calculated flow varied between 1.75µl/min and 25.9µl/min and was highly correlated (r2= 0.995) with the imposed flow by a syringe pump. In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between -11% and 36% (mean±sd 5.7±18.5%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 0.9 µL/min and 13.2µL/min. Conclusion: This adaptive optics LDV prototype (aoLDV) allows the measurement of absolute retinal blood flow derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.

opencc-by-4.0Feb 2017View details →

Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks

The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017. For each network the following data are provided: - Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs). - RBC trajectories describing the motion of individual RBCs through the microvascular networks. - The data is stored as a graph, i.e. vertices connected by edges. - Details regarding the simulation parameters can be found in Schmid et al., 2017. - Data format (pickle - files containing python dictonairies). Microvascular networks: edgesDict.pkl: dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um]) verticesDict.pkl: dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg]) Additional comments on dictionary keys: - pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details) - tuple: connectivity of graph, tuple of vertices - httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned. - nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown - htt: tube hematocrit - nRBC: number of red blood cells - points: list of tortuous vessel coordinates per edge - diameters: local diameter measurements associated to the 'points' key. RBC trajectories: RBC_trajectories.pkl: dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft Additional comments on dictionary keys per RBC: - RBCleft: bool to indicate that RBC left the computational domain - edges: edge indices through which the RBC moves on its way through the vasculature - pressure: pressure [mmHg] values at the nodes along the RBC trajectory - times: time [ms] the RBC spends in the respective edge segment - nkindsMod: nkind at the nodes along the RBC trajectory  - lengths: cummulative length travelled [um]

opencc-by-4.0Jul 2018View details →

Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature

The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse. Further notes on the simulation results: - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>). - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>). - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively. - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>) File format: pickle (Python) Files 1 - 4 (Time-averaged simulation results): Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2 Each compressed folder contains two files: edgesDict.pkl: dictionary with edge/vessel related data:  <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> verticesDict.pkl: dictionary with vertex/bifurcation related data: <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul>   File 5 (in vivo RBC velocity measurements): Filename: measurementDict.pkl keys: <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> Data structure: list of list, e.g. daughter vessel 1: [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...] bif.: bifurcation, measure.: measurement. The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2').

opencc-by-4.0Feb 2018View details →

Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments"

The data in this archive was acquired to investigate the coupling between cerebral blood flow and cerebral blood volume across different vascular compartments. These results will form the basis of a forthcoming publication. Please reference this dataset (version v1.1.0) if you use it in your work. Wesolowski R, Blockley NP, Driver ID, Francis ST, Gowland PA. Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments". Zenodo 2018. doi: 10.5281/zenodo.1411018.  This dataset contains measurements of the haemodynamic responses of different compartments to a visual stimulus (8Hz red LED goggles, 19.2s ON, 40.8s OFF). Time course data are cycle averaged for the following haemodynamic properties; <ol> <li>Arterial cerebral blood volume (CBVa) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBVa.</li> <li>Cerebral blood flow (CBF) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBF.</li> <li>Total cerebral blood volume (CBVtot) measured using bolus injections of a Gadolinium based contrast agent combined with T2* weighed gradient echo EPI.</li> </ol> In addition, weighted mean and standard deviation of the changes in these parameters are presented using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. Weighting is performed with respect to the number of voxels present in each of the subjects regions of interest. Time course data were extracted from two different regions of interest; <ol> <li>ROICBF: defined using a CBF localiser</li> <li>ROICOMMON: defined using the overlap of CBF, CBVa and CBVtot localisers</li> </ol> Furthermore, the transit times for CBF and CBVa were estimated for an average stimulus cycle and mean values extracted using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. These data were extracted from ROICBF. Changes - Addition of propagation of uncertainty for CBVv and the Grubb constants alpha_tot and alpha_a.

opencc-by-4.0Jul 2021View details →

Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.

DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios. Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>) 1. Related references: The data set is published in context with the manuscript:  [1] The severity of microstrokes depends on local vascular topology and baseline perfusion.  F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208 The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in: [2] The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426 The bi-phasic blood flow model for realistic MVNs has first been published in: [3] Depth-dependent flow and pressure characteristics in cortical microvascular networks. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392 For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3]. 2. Requirements (software): All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”). 3. Content: All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "tar –jxf  ARCHIVE_NAME" 3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files): SimulationResults_Baseline.tar.bz2: Folders: MVN1, MVN2 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (generated from prepare_Figure4.py), data_spatial_distribution_AVfactor.pkl (generated from plot_Figure4.py) SimulationResults_SingleCapillaryOcclusions.tar.bz2: Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (only folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out) SimulationResults_MultiCapillaryOcclusions.tar.bz2: Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl 3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts): For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses. helperFunctions.py: various functions used by the other analysis scripts plot_Figure1_and_Figure1-supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/* plot_Figure2_and_Figure2_supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/* plot_Figure3.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl  Output: Figures/Figure_3/* prepare_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl, Output: SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl plot_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/* Output: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/* plot_Figure5.py: Input: SimulationResults_Baseline/MVN*/*  Output: Figures/Figure_5/* plot_Figure6.py: Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/* 4. Attributes stored in python dictionaries: 4a. Baseline: verticesDict: contains all relevant information and data stored at vertices. <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> edgesDict: contains all relevant information and data stored at edges. <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm3 ms-1]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm3]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch: contains all flow path from DA main brain to AV main branch. For details see [1]. <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> data_spatial_distribution_AVfactor: contains information on the spatial distribution of venule-sided capillaries (AV-factor  > 0.5). For details see [1]. <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> 4b. Occlusion scenarios (both single- and multi-capillary occlusions): verticesDict: <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> edgesDict: <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm3 ms-1] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased: contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --> function convert_pathsDict_to_unique_vertexSequence. <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>

opencc-by-4.0Jan 2023View details →

Low-frequency oscillations of blood flow velocity

figS4a-d.h5 - Intensity profiles obtained by scanning along a line (described by the below attribute) placed along a FITCdextran stained vessel. Record features traces of individual red blood cells from which the blood flow velocity in figure S4 was obtained. File contains single dataset: 1 - Imaging data_200807_111309 -> voltage from PMT collecting FITCdextran fluorescence signal     The dataset features two attributes:     1 - Display Time -> hologram dwell time in microseconds     2 - Image Dimentions -> dimensions of the full resolution image used prior the line-scans has been selected      3 - ROI_XY -> coordinates of individual pixels of the scanning trajectory

dryad40/100

Data for: Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients

Open the record for dataset details and reuse information.

publicOct 2024View details →

zenodo36/100

Distribution of blood flow oscillation across the Doppler shift evaluated by the proposed approach with local pressure test.

A step-wise increase in local pressure is known to cause a gradual change in the parameters of capillary blood flow in the upper layers of the skin, and can also affect the measurement results of various optical methods. The impact of the procedure has several subsequent effects, such as mechanical compression of vessels, and neurological and metabolic compensating mechanisms like pressure-induced vasodilation, which maintain the homeostasis of the skin during moderate levels of external pressure and tissue hypoxia. To examine how those effects are translated to the blood flow registering in different ranges of the Doppler spectra, we have developed a 3D-printed pressure distribution tool compatible with the developed sensor which was used, and equipped with a set of weights.  During the main series of measurements, the weights were placed into the PDT in a step-wise manner to achieve the following values of pressure applied: 10 mmHg, 30 mmHg, 90 mmHg, 150 mmHg, 210 mmHg. At the end of the procedure, the load was reduced back to 30 mmHg. Experiments were conducted with the participation of 7 healthy volunteers with 10 min LDF recording for each step. To estimate the prominence of the observed effects and substantiate the measuring routing, several preliminary experiments were also conducted where the set of values of pressure was applied with a step-wise increase and then decrease with about 2 min of LDF recordings for each step. Published in IEEE Transactions on Biomedical Engineering "Diagnosis of skin vascular complications revealed by time-frequency analysis and laser Doppler spectrum decomposition", Zherebtsov et al.

opencc-by-4.0Oct 2021View details →

zenodo36/100

Dynamic light scattering differentiate parameters of blood flow

This dataset demonstrates blood perfusion recordings measurements on the 3rd fingers and wrists simultaneously (sitting position) in volunteers of three groups: healthy volunteers younger group (20 years old), healthy volunteers younger group (~55 years old), patients with Diabetes Type 2 (~55 years old).

opencc-by-4.0Aug 2021View details →

zenodo36/100

Synthetic velocity profiles for simulations of blood flow in the aorta

Synthetic dataset of aortic velocity profiles, suitable to be used for numerical simulations of blood flow. Please refer to the profile number ID when using it.

opencc-by-4.0Oct 2022View details →

The Effects of Dexmedetomidine and Propofol on Cerebral Blood Flow and Brain Oxygenation During Deep Brain Stimulation

ClinicalTrials.gov study NCT01200433. IPD Sharing: NO. Countries: 1. Publications: 1.

Cadmium-zinc-telluride (CZT) Imaging of Myocardial Blood Flow (MBF) (SPECT MBF)

ClinicalTrials.gov study NCT02280941. IPD Sharing: NO. Countries: 1. Publications: 3.

Doppler Ultrasound Probe for Blood Flow Detection in Severe Upper Gastrointestinal Hemorrhage

ClinicalTrials.gov study NCT00732212. IPD Sharing: NO. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

Low-Load Blood Flow Restriction Training vs Traditional Resistance Training Exercises Following ACLR Surgery

ClinicalTrials.gov study NCT06480032. IPD Sharing: YES. Countries: 1. Publications: 3.

Cerebral Blood Flow During Propofol Anaesthesia

ClinicalTrials.gov study NCT02951273. IPD Sharing: NO. Countries: 1. Publications: 13.

Effect of Peripheral Neuromodulation on Vaginal Blood Flow

ClinicalTrials.gov study NCT04384172. IPD Sharing: NO. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

Effects of Blood Flow Restriction Exercises on Lumbar Muscle Endurance and Balance in Healthy Young Adults

ClinicalTrials.gov study NCT07182812. IPD Sharing: YES. Countries: 1. Publications: 7.