scholarly journals Deep Learning Assisted Zonal Adaptive Aberration Correction

2021 ◽  
Vol 8 ◽  
Author(s):  
Biwei Zhang ◽  
Jiazhu Zhu ◽  
Ke Si ◽  
Wei Gong
Keyword(s):  
Deep Learning ◽  
Adaptive Optics ◽  
Degrees Of Freedom ◽  
Correction Method ◽  
Tissue Imaging ◽  
Optical Aberrations ◽  
Volume Imaging ◽  
Adaptive Correction ◽  
Average Mean Square Error ◽  
Deep Tissue Imaging

Deep learning (DL) has been recently applied to adaptive optics (AO) to correct optical aberrations rapidly in biomedical imaging. Here we propose a DL assisted zonal adaptive correction method to perform corrections of high degrees of freedom while maintaining the fast speed. With a trained DL neural network, the pattern on the correction device which is divided into multiple zone phase elements can be directly inferred from the aberration distorted point-spread function image in this method. The inference can be completed in 12.6 ms with the average mean square error 0.88 when 224 zones are used. The results show a good performance on aberrations of different complexities. Since no extra device is required, this method has potentials in deep tissue imaging and large volume imaging.

scholarly journals Adaptive optics allows 3D STED-FCS measurements in the cytoplasm of living cells

2019 ◽  
Author(s):  
Aurélien Barbotin ◽  
Silvia Galiani ◽  
Iztok Urbančič ◽  
Christian Eggeling ◽  
Martin Booth
Keyword(s):  
Adaptive Optics ◽  
Molecular Diffusion ◽  
Three Dimensional ◽  
Super Resolution ◽  
Correction Method ◽  
Living Cells ◽  
Correlation Spectroscopy ◽  
Stimulated Emission ◽  
Optical Aberrations ◽  
Extreme Sensitivity

Fluorescence correlation spectroscopy in combination with super-resolution stimulated emission depletion microscopy (STED-FCS) is a powerful tool to investigate molecular diffusion with sub-diffraction resolution. It has been of particular use for investigations of two dimensional systems like cell membranes, but has so far seen very limited applications to studies of three-dimensional diffusion. One reason for this is the extreme sensitivity of the axial (3D) STED depletion pattern to optical aberrations. We present here an adaptive optics-based correction method that compensates for these aberrations and allows STED-FCS measurements in the cytoplasm of living cells.

Multiple guide stars optimization in conjugate adaptive optics for deep tissue imaging

2020 ◽  
Vol 459 ◽  
pp. 124891
Author(s):  
Chenxue Wu ◽  
Jiajia Chen ◽  
Biwei Zhang ◽  
Yao Zheng ◽  
Xinpei Zhu ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Tissue Imaging ◽  
Deep Tissue ◽  
Deep Tissue Imaging

scholarly journals Improving the Way We See: Adaptive Optics Based Optical Microscopy for Deep-Tissue Imaging

2021 ◽  
Vol 9 ◽  
Author(s):  
Pranoy Sahu ◽  
Nirmal Mazumder
Keyword(s):  
Adaptive Optics ◽  
Cellular Level ◽  
Optical Methods ◽  
Tissue Imaging ◽  
Excitation Beam ◽  
Recent Developments ◽  
Deep Tissue Imaging ◽  
And Function ◽  
Tools And Techniques ◽  
Two Photon Fluorescence

With the recent developments in optical imaging tools and techniques, scientists are now able to image deeper regions of the tissue with greater resolution and accuracy. However, light scattering while imaging deeper regions of a biological tissue remains a fundamental issue. Presence of lipids, proteins and nucleic acids in the tissue makes it inhomogeneous for a given wavelength of light. Two-photon fluorescence (TPF) microscopy supplemented with improved invasive optical tools allows functional imaging in awake behaving mammals in an unprecedented manner. Similarly, improved optical methods conjugated with previously existing scanning laser ophthalmoscopy (SLO) has paved diffraction-limited retinal imaging. With the evolving technology, scientists are now able to resolve biological structures and function at the sub-cellular level. Wavefront correcting methods like adaptive optics (AO) has been implemented in correcting tissue or optical-based distortions, shaping the excitation beam in 3D-holography to target multiple neurons. And more recently, AO-based SLO is implemented for eye imaging both in research and clinical settings. In this review, we discuss some of the recent improvements in TPF microscopy with the application of AO for wavefront corrections and its recent application in brain imaging as well as ophthalmoscopy.

scholarly journals High-resolution structural and functional deep brain imaging using adaptive optics three-photon microscopy

2021 ◽  
Author(s):  
Lina Streich ◽  
Juan Carlos Boffi ◽  
Ling Wang ◽  
Khaleel Alhalaseh ◽  
Matteo Barbieri ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Multiphoton Microscopy ◽  
Motion Artifacts ◽  
Tissue Imaging ◽  
Mammalian Brain ◽  
Neural Cells ◽  
Photon Excitation ◽  
Deep Tissue Imaging ◽  
Fibrous Astrocytes ◽  
And Function

AbstractMultiphoton microscopy has become a powerful tool with which to visualize the morphology and function of neural cells and circuits in the intact mammalian brain. However, tissue scattering, optical aberrations and motion artifacts degrade the imaging performance at depth. Here we describe a minimally invasive intravital imaging methodology based on three-photon excitation, indirect adaptive optics (AO) and active electrocardiogram gating to advance deep-tissue imaging. Our modal-based, sensorless AO approach is robust to low signal-to-noise ratios as commonly encountered in deep scattering tissues such as the mouse brain, and permits AO correction over large axial fields of view. We demonstrate near-diffraction-limited imaging of deep cortical spines and (sub)cortical dendrites up to a depth of 1.4 mm (the edge of the mouse CA1 hippocampus). In addition, we show applications to deep-layer calcium imaging of astrocytes, including fibrous astrocytes that reside in the highly scattering corpus callosum.

scholarly journals Overcoming the penetration depth limit in optical microscopy: Adaptive optics and wavefront shaping

2019 ◽  
Vol 12 (04) ◽  
pp. 1930002 ◽  
Author(s):  
Cheolwoo Ahn ◽  
Byungjae Hwang ◽  
Kibum Nam ◽  
Hyungwon Jin ◽  
Taeseong Woo ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Microscopy ◽  
Adaptive Optics ◽  
Single Cells ◽  
Tissue Imaging ◽  
Future Research ◽  
Deep Tissue ◽  
Depth Limit ◽  
Deep Tissue Imaging ◽  
Wavefront Shaping

Despite the unique advantages of optical microscopy for molecular specific high resolution imaging of living structure in both space and time, current applications are mostly limited to research settings. This is due to the aberrations and multiple scattering that is induced by the inhomogeneous refractive boundaries that are inherent to biological systems. However, recent developments in adaptive optics and wavefront shaping have shown that high resolution optical imaging is not fundamentally limited only to the observation of single cells, but can be significantly enhanced to realize deep tissue imaging. To provide insight into how these two closely related fields can expand the limits of bio imaging, we review the recent progresses in their performance and applicable range of studies as well as potential future research directions to push the limits of deep tissue imaging.

scholarly journals Simplifying the detection of optical distortions by machine learning

2019 ◽  
Vol 13 (03) ◽  
pp. 2040001
Author(s):  
Shuwen Hu ◽  
Lejia Hu ◽  
Biwei Zhang ◽  
Wei Gong ◽  
Ke Si
Keyword(s):  
Machine Learning ◽  
Adaptive Optics ◽  
High Speed ◽  
Mean Squared Error ◽  
Tissue Imaging ◽  
Wavefront Sensor ◽  
Optical Distortion ◽  
Detection Accuracy ◽  
Deep Tissue Imaging

Adaptive optics has been widely used in biological science to recover high-resolution optical image deep into the tissue, where optical distortion detection with high speed and accuracy is strongly required. Here, we introduce convolutional neural networks, one of the most popular machine learning models, into Shack–Hartmann wavefront sensor (SHWS) to simplify optical distortion detection processes. Without image segmentation or centroid positioning algorithm, the trained network could estimate up to 36th Zernike mode coefficients directly from a full SHWS image within 1.227[Formula: see text]ms on a personal computer, and achieves prediction accuracy up to 97.4%. The simulation results show that the average root mean squared error in phase residuals of our method is 75.64% lower than that with the modal-based SHWS method. With the high detection accuracy and simplified detection processes, this work has the potential to be applied in wavefront sensor-based adaptive optics for in vivo deep tissue imaging.

Two-Dimensional Near-Infrared and Shortwave Infrared Molecular Aggregates: Taking Kasha’s Model to the Next Dimension

2019 ◽  
Author(s):  
Arundhati Deshmukh ◽  
Danielle Koppel ◽  
Chern Chuang ◽  
Danielle Cadena ◽  
Jianshu Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Photophysical Properties ◽  
Satellite Telemetry ◽  
Transition Dipole Moment ◽  
Short Wave ◽  
Tissue Imaging ◽  
Transition Dipole ◽  
Deep Tissue Imaging ◽  
Excitonic States ◽  
Shortwave Infrared

Technologies which utilize near-infrared (700 – 1000 nm) and short-wave infrared (1000 – 2000 nm) electromagnetic radiation have applications in deep-tissue imaging, telecommunications and satellite telemetry due to low scattering and decreased background signal in this spectral region. However, there are few molecular species, which absorb efficiently beyond 1000 nm. Transition dipole moment coupling (e.g. J-aggregation) allows for redshifted excitonic states and provides a pathway to highly absorptive electronic states in the infrared. We present aggregates of two cyanine dyes whose absorption peaks redshift dramatically upon aggregation in water from ~ 800 nm to 1000 nm and 1050 nm with sheet-like morphologies and high molar absorptivities (e ~ 10<sup>5 </sup>M<sup>-1</sup>cm<sup>-1</sup>). To describe this phenomenology, we extend Kasha’s model for J- and H-aggregation to describe the excitonic states of <i> 2-dimensional aggregates</i> whose slip is controlled by steric hindrance in the assembled structure. A consequence of the increased dimensionality is the phenomenon of an <i>intermediate </i>“I-aggregate”, one which redshifts yet displays spectral signatures of band-edge dark states akin to an H-aggregate. We distinguish between H-, I- and J-aggregates by showing the relative position of the bright (absorptive) state within the density of states using temperature dependent spectroscopy. Our results can be used to better design chromophores with predictable and tunable aggregation with new photophysical properties.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngbin Na ◽  
Do-Kyeong Ko
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Degrees Of Freedom ◽  
Image Transmission ◽  
Data Traffic ◽  
Free Space Optical ◽  
Learning Classifier ◽  
Communication Capacity ◽  
Data Rates ◽  
Mode Space

AbstractStructured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.

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