scholarly journals Protocol for the Design and Assembly of a Light Sheet Light Field Microscope

2019 ◽  
Vol 2 (3) ◽  
pp. 56 ◽  
Author(s):  
Jorge Madrid-Wolff ◽  
Manu Forero-Shelton
Keyword(s):  
Neural Activity ◽  
Light Field ◽  
Signal To Noise Ratio ◽  
Light Sheet ◽  
High Temporal Resolution ◽  
Single Camera ◽  
Signal To Noise ◽  
Light Sheet Microscopy ◽  
Recent Method ◽  
Noise Ratio

Light field microscopy is a recent development that makes it possible to obtain images of volumes with a single camera exposure, enabling studies of fast processes such as neural activity in zebrafish brains at high temporal resolution, at the expense of spatial resolution. Light sheet microscopy is also a recent method that reduces illumination intensity while increasing the signal-to-noise ratio with respect to confocal microscopes. While faster and gentler to samples than confocals for a similar resolution, light sheet microscopy is still slower than light field microscopy since it must collect volume slices sequentially. Nonetheless, the combination of the two methods, i.e., light field microscopes that have light sheet illumination, can help to improve the signal-to-noise ratio of light field microscopes and potentially improve their resolution. Building these microscopes requires much expertise, and the resources for doing so are limited. Here, we present a protocol to build a light field microscope with light sheet illumination. This protocol is also useful to build a light sheet microscope.

scholarly journals Self-Reset Image Sensor With a Signal-to-Noise Ratio Over 70 dB and Its Application to Brain Surface Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Thanet Pakpuwadon ◽  
Kiyotaka Sasagawa ◽  
Mark Christian Guinto ◽  
Yasumi Ohta ◽  
Makito Haruta ◽  
...  
Keyword(s):  
Neural Activity ◽  
Signal To Noise Ratio ◽  
Image Sensor ◽  
Oxide Semiconductor ◽  
High Signal ◽  
Unstable State ◽  
Signal To Noise ◽  
Compact Size ◽  
Noise Ratio

In this study, we propose a complementary-metal-oxide-semiconductor (CMOS) image sensor with a self-resetting system demonstrating a high signal-to-noise ratio (SNR) to detect small intrinsic signals such as a hemodynamic reaction or neural activity in a mouse brain. The photodiode structure was modified from N-well/P-sub to P+/N-well/P-sub to increase the photodiode capacitance to reduce the number of self-resets required to decrease the unstable stage. Moreover, our new relay board was used for the first time. As a result, an effective SNR of over 70 dB was achieved within the same pixel size and fill factor. The unstable state was drastically reduced. Thus, we will be able to detect neural activity. With its compact size, this device has significant potential to become an intrinsic signal detector in freely moving animals. We also demonstrated in vivo imaging with image processing by removing additional noise from the self-reset operation.

Optical measurement of the corneal oscillation for the determination of the intraocular pressure

2019 ◽  
Vol 64 (4) ◽  
pp. 471-480 ◽  
Author(s):  
Jan Osmers ◽  
Michael Sorg ◽  
Andreas Fischer
Keyword(s):  
Intraocular Pressure ◽  
Optical Measurement ◽  
Signal To Noise Ratio ◽  
Significant Risk ◽  
High Signal ◽  
High Temporal Resolution ◽  
Signal To Noise ◽  
Handheld Device ◽  
Noise Ratio ◽  
Reference Sensor

Abstract Motivation Glaucoma is currently the most common irreversible cause of blindness worldwide. A significant risk factor is an individually increased intraocular pressure (IOP). A precise measurement method is needed to determine the IOP in order to support the diagnosis of the disease and to monitor the outcome of the IOP reduction as a medical intervention. A handheld device is under development with which the patient can perform self-measurements outside the clinical environment. Method For the measurement principle of the self-tonometer the eye is acoustically excited to oscillate, which is analyzed and attributed to the present IOP. In order to detect the corneal oscillation, an optical sensor is required which meets the demands of a compact, battery driven self-tonometer. A combination of an infrared diode and a phototransistor provides a high-resolution measurement of the corneal oscillation in the range of 10 μm–150 μm, which is compared to a reference sensor in the context of this study. By means of an angular arrangement of the emitter and the detector, the degree of reflected radiation of the cornea can be increased, allowing a measurement with a high signal-to-noise ratio. Results By adjusting the angle of incidence between the detector and the emitter, the signal-to-noise ratio was improved by 40 dB which now allows reasonable measurements of the corneal oscillation. For low amplitudes (10 μm) the signal-to-noise ratio is 10% higher than that of the commercial reference sensor. On the basis of amplitude variations at different IOP levels, the estimated standard uncertainty amounts to <0.5 mm Hg in the physiological pressure range with the proposed measuring approach. Conclusion With a compact and cost-effective approach, that suits the requirements for a handheld self-tonometer, the corneal oscillation can be detected with high temporal resolution. The cross-sensitivity of the sensor concept concerning a distance variation can be reduced by adding a distance sensor. Existing systematic influences of corneal biomechanics will be integrated in the sensor concept as a consecutive step.

scholarly journals A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

2015 ◽  
Vol 8 (10) ◽  
pp. 11139-11170
Author(s):  
A. J. Manninen ◽  
E. J. O'Connor ◽  
V. Vakkari ◽  
T. Petäjä
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Background Correction ◽  
Data Availability ◽  
Horizontal Wind ◽  
Limiting Factor ◽  
High Temporal Resolution ◽  
Signal To Noise ◽  
Uncertainty Estimates ◽  
Noise Ratio

Abstract. Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allow turbulent properties to be obtained from studying the variation in velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Any bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. The reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.

scholarly journals Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

2018 ◽  
Author(s):  
Ming Bo Cai ◽  
Nicolas W. Schuck ◽  
Jonathan W. Pillow ◽  
Yael Niv
Keyword(s):  
Neural Activity ◽  
Signal To Noise Ratio ◽  
Activity Patterns ◽  
Covariance Structure ◽  
Neural Population ◽  
Similarity Analysis ◽  
Imaging Data ◽  
Signal To Noise ◽  
Representational Similarity Analysis ◽  
Noise Ratio

AbstractThe activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degree of similarity between these neural activity patterns in response to different events is used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).Author summaryWe show the severity of the bias introduced when performing representational similarity analysis (RSA) based on neural activity pattern estimated within imaging runs. Our Bayesian RSA method significantly reduces the bias and can learn a shared representational structure across multiple participants. We also demonstrate its extension as a new multi-class decoding tool.

scholarly journals Lens-free Multi-Laser Spectral Light-Field Fusion Microscopy

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Farnoud Kazemzadeh ◽  
Alexander Wong
Keyword(s):  
Light Field ◽  
Signal To Noise Ratio ◽  
Field Of View ◽  
Large Field ◽  
Light Fields ◽  
Signal To Noise ◽  
Pulsed Mode ◽  
Noise Ratio ◽  
Spectral Complex

<p>We present a device and method for performing lens-free spectral<br />light-field fusion microscopy at sub-pixel resolutions while taking<br />advantage of the large field-of-view capability. A collection of<br />lasers at different wavelengths is used in pulsed mode and enables<br />the capture of interferometric light-field encodings of a specimen<br />placed near the detector. Numerically fusing the spectral complex<br />light-fields obtained from the encodings produces an image of the<br />specimen at higher resolution and signal-to-noise-ratio while suppressing<br />various aberrations and artifacts.</p>

scholarly journals A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

2016 ◽  
Vol 9 (2) ◽  
pp. 817-827 ◽  
Author(s):  
Antti J. Manninen ◽  
Ewan J. O'Connor ◽  
Ville Vakkari ◽  
Tuukka Petäjä
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Background Correction ◽  
Data Availability ◽  
Horizontal Wind ◽  
Limiting Factor ◽  
High Temporal Resolution ◽  
Signal To Noise ◽  
Uncertainty Estimates ◽  
Noise Ratio

Abstract. Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allows turbulent properties to be obtained from studying the variation in radial velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Any bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. The reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.

scholarly journals Comparing volumetric reconstruction algorithms for light field imaging of high signal-to-noise ratio neuronal calcium transients

2020 ◽  
Author(s):  
Carmel L. Howe ◽  
Peter Quicke ◽  
Pingfan Song ◽  
Herman Verinaz Jadan ◽  
Pier Luigi Dragotti ◽  
...  
Keyword(s):  
Light Field ◽  
Signal To Noise Ratio ◽  
Brain Slices ◽  
Three Dimensions ◽  
High Signal ◽  
Reconstruction Algorithms ◽  
Signal To Noise ◽  
Neuronal Processes ◽  
Noise Ratio ◽  
Fast Light

AbstractLight field microscopy (LFM) enables fast, light efficient, volumetric imaging of neuronal activity with functional fluorescence indicators. Here we apply LFM to single-cell and bulk-labeled imaging of the red calcium dye, CaSiR-1 in acute mouse brain slices. We compare two common light field volume reconstruction algorithms: synthetic refocusing and Richardson-Lucy 3D deconvolution. We compare temporal signal-to-noise ratio (SNR) and spatial signal confinement between the two LFM algorithms and conventional widefield image series. Both algorithms can resolve calcium signals from neuronal processes in three dimensions. Increasing deconvolution iteration number improves spatial signal confinement but reduces SNR compared to synthetic refocusing.

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