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30 results for “Adaptive optics”
Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics
<p><strong>Purpose</strong>: 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<br> <strong>Methods</strong>: 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<br> <strong>Results</strong>: In the in vitro experiment, the calculated flow varied between 1.75µl/min and 25.9µl/min and was highly correlated (r<sup>2</sup>= 0.995) with the imposed flow by a syringe pump.<br> 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.</p> <p><strong>Conclusion</strong>: 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.</p>
Comprehensive Automatic Processing and Analysis of Adaptive Optics Flood Illumination Retinal Images
<p>A collaborative research group has established this database to support AO-FIO image utilization and evaluation of photoreceptor detection. <br> Please cite the following publication when using the database:</p> <p>Eva Valterova, Jan D. Unterlauft, Mike Francke, Toralf Kirsten, Radim Kolar, and Franziska G. Rauscher, "Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects," Biomed. Opt. Express <strong>14</strong>, 945-970 (2023)<br> <br> The database can be utilized in connection with our application MATADOR for AO-FIO image registration and analysis, which is freely available on:</p> <p>https://github.com/evavalterova/MATADOR.git</p> <p>The database includes</p> <ul> <li>over 200 flood illumination adaptive optics images of 10 normal healthy subjects. Each folder includes 10 images of the right eye (denoted by OD) and 10 images of the left eye (denoted by OS) with their preliminary determined retinal position during image acquisition.</li> <li>foveal and peripheral patches. Each consists of 40 cropped regions from the set of 200 images. In each cropped region are manually labeled positions of photoreceptors by three evaluators.</li> <li>axial lengths of 10 subjects in ".xlsx" file<br> </li> </ul>
Optimal modes for wavefront sensorless adaptive optics. Turbulence- and oocyte-induced phase screens and Mathematica notebooks.
<p>This notebook and phase screens constitute a numerical experiment to calculate the error of the wavefront approximation using first N modes of the a) Zernike and b) Lukosz-Braat polynomials, c) SVD modes obtained with respect to the gradient-dot product, and the d) eigenfunctions of the Laplace operator with the Neumann boundary conditions. It's a complementary material to a paper submitted to Optics Express.</p>
Dataset: Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography
<p>The ptychography datasets and processed wavefront data in support of the publication "Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography". File are in the HDF format and contain a number of datasets detailed below. If you require more information, please contact the corresponding author of the publication or thomas.moxham@eng.ox.ac.uk</p>
MAVIS adaptive optics system matrices dataset
<p>MAVIS adaptive optics system matrices dataset.</p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </p>
Label-free adaptive optics single-molecule localization microscopy for whole zebrafish
<p>The specimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 μm with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to whole zebrafish that cause the loss of localization points owing to severe tissue aberrations.</p>
Novel Optical Surface Image Guidance System for Beam-Gated Online Adaptive SBRT Delivery in Mobile Lower Lung and Upper Abdominal Malignancies
ClinicalTrials.gov study NCT05030454. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Adaptive optical imaging with entangled photons
Open the record for dataset details and reuse information.
Data from: Persistent firing and adaptation in optic-flow-sensitive descending neurons
<p>A general principle of sensory systems is that they adapt to prolonged stimulation by reducing their response over time. Indeed, in many visual systems, including higher-order motion sensitive neurons in the fly optic lobes and the mammalian visual cortex, a reduction in neural activity following prolonged stimulation occurs. In contrast to this phenomenon, the response of the motor system controlling flight maneuvers persists following the offset of visual motion. It has been suggested that this gap is caused by a lingering calcium signal in the output synapses of optic lobe neurons. However, whether this directly affects the responses of the post-synaptic descending neurons, leading to the observed behavioral output, is not known. We use extracellular electrophysiology to record from optic flow sensitive descending neurons in response to prolonged wide-field stimulation. We find that, as opposed to most sensory and visual neurons, and in particular to the motion vision sensitive neurons in the brains of flies and mammals, the descending neurons show little adaption during stimulus motion. In addition, we find that the optic flow sensitive descending neurons display persistent firing, or an after-effect, following the cessation of visual stimulation, consistent with the lingering calcium signal hypothesis. However, if the difference in after-effect is compensated for, subsequent presentation of stimuli in a test-adapt-test paradigm reveals adaptation to visual motion. Our results thus show a combination of adaptation and persistent firing, in the neurons that project to the thoracic ganglia, and thereby control behavioral output.</p>
Adaptive Optics Retinal Imaging
ClinicalTrials.gov study NCT05370287. IPD Sharing: YES. Countries: 1. Publications: 7.
Adaptive Optics Retinal Imaging
ClinicalTrials.gov study NCT02317328. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Retinal Imaging by Adaptive Optics in Healthy Eyes and During Retinal and General Diseases
ClinicalTrials.gov study NCT01546181. IPD Sharing: Not stated. Countries: 1. Publications: 20.
Advanced OCT and Adaptive Optics Imaging in Retinal Disease (The ACAD Study)
ClinicalTrials.gov study NCT02828215. IPD Sharing: NO. Countries: 1. Publications: 1.
Identification of Retinal Perivascular Inflammation in Patients With Multiple Sclerosis Using Adaptive Optics (RETIMUS)
ClinicalTrials.gov study NCT04289909. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility of Measurement of Optical Aberrations in Hyperopia by Using an Adaptive Optics Visual Simulator (AOVIS-I)
ClinicalTrials.gov study NCT01884805. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Persistent firing and adaptation in optic-flow-sensitive descending neurons
Open the record for dataset details and reuse information.
Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics
<p><strong>Data set of measurements related to the following:</strong></p> <p><strong>Purpose: </strong>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.</p> <p><strong>Methods: </strong>This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes©,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 human retinal venous junctions were done to assess the device.</p> <p><strong>Results: </strong>In the in vitro experiment, the calculated flow varied between 1.75 μl/min and 25.9 μl/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 −25% and 17% (mean±sd −2 ± 17%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 1.3 μL/min and 28.7 μL/min</p> <p><strong>Conclusion: </strong>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.</p>
Sensing and Imaging Dataset for Ground-based Adaptive Optics (SIDGAO)
<p>our synthetic Sensing and Imaging Dataset for Ground-based Adaptive Optics.</p> <p>This Project is Partially Supported by the National Natural Science Foundation of China(No.62005221) and the State Key Laboratory of Applied Optics(No.SKLAO2021001A04).</p>
An Adaptable Soft-mold Embossing Process for Fabricating Optically-accessible, Microfeature-based Culture Systems and Application toward Liver Stage Antimalarial Compound Testing
GEO Series GSE144599. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
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