A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with a traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to large field of view images and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pre-trained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

https://www.biorxiv.org/content/biorxiv/early/2022/12/02/2022.11.30.518528.full.pdf

A deep learning approach for improving two-photon vascular imaging speeds

The wavelength 1550 nm is widely used in fiber-optic communications and imaging systems. Avalanche photodiodes (APDs) offer high sensitivity for detecting this wavelength. However, the noise caused by the stochastic nature of the impact ionization gain mechanism can temper the benefit of amplifying the signal. Here, we report a separate absorption, charge, and multiplication APD that uses a two-step staircase multiplication region exhibiting a near-unity excess noise factor. At a gain of four, the excess noise factor is approximately three times lower than conventionally used InGaAs/InP separate absorption, charge, and multiplication APDs. This improved excess noise coupled with 1550-nm operation offers the potential for enhanced performance in several key application areas. 

Separate absorption, charge, and multiplication staircase avalanche photodiodes

Spectral confocal technology uses chromatic aberration which is generated by a dispersion lens to detect surface shape. The axial dispersion generated by the dispersion lens will affect the measurement range of the whole spectral confocal displacement sensor. The refractive index of an axial GRIN (gradient index, GRIN) lens varies non-homogeneous along the axial direction and is constantly perpendicular to an optical axis of the plane. The paper explored the design of a dispersive objective lens for a spectral confocal displacement sensor based on the GRIN lens. Firstly, the optical power and axial dispersion models of the GRIN lens are established. The axial dispersion can be realized by focusing the light of different wavelengths at different positions of the optical axis. Secondly, based on the optic power and dispersion function of the GRIN lens, the refractive index distribution of the GRIN lens and the simulation design of the dispersive objective lens is obtained by using MATLAB and ZEMAX software respectively. Finally, the GRIN dispersive objective lens is optimized by setting different merit function operands. The experimental results show that the axial GRIN lens can achieve a focal shift of 1130μm within the wavelength range from 486nm to 656nm. Moreover, the linearity of the lens behaves well. All the blur spot is much smaller than the airy spot. The lens has well-focused as well as high-precision. The research results provide a reference and theoretical basis for the application of the GRIN lens in spectral confocal technology.

Design of dispersive objective lens for spectral confocal displacement sensor based on GRIN lens

The inverse correlation time (ICT) is a key quantity in laser speckle contrast imaging (LSCI) measurements. Traditionally, ICT is regarded as a metric of blood flow, such as speed or perfusion. However, we highlight that ICT not only contains important information about blood flow, but also reflects the underlying structure of the vascular network. In the past, ICT has been found to be correlated with vessel diameter. Here, we further report that ICT exhibits a different sensitivity to blood flow depending on vessel orientation. Specifically, ICT is more sensitive to blood flow speed changes in vessels descending from or arising to the tissue surface, compared with those laying parallel to the surface. Those findings shift our understanding of ICT from purely blood flow to a combination of blood flow and vascular network structure. We also develop theories to facilitate the study of vascular network’s impact on ICT.

Interpreting Inverse Correlation Time: from Blood flow to Vascular Network

In some applications, the object space of light field imaging system is restrictive, such as industrial and medical endoscopes. If the traditional light field imaging system is used in the restrictive object space (ROS) directly but without any specific considerations, the ROS will lead to severe microlens image distortions and then affects light field decoding, calibration and 3D reconstruction. The light field imaging in restrictive object space (ROS-LF) is complicated but significant. In this paper, we first deduce that the reason of the microlens image deviation is the position variation of the angular plane, then we propose the flexible angular plane for ROS-LF, while in the traditional light field the angular plane always coincides with the main lens plane. Subsequently, we propose the microlens image non-distortion principle for ROS-LF and introduce the ROS-LF imaging principle. We demonstrate that the difference is an aperture constant term between the ROS-LF and traditional light field imaging models. At last, we design a ROS-LF simulated system and calibrate it to verify principles proposed in this paper.

Light Field Imaging in the Restrictive Object Space based on Flexible Angular Plane

: Optical endoscopy has emerged as an indispensable clinical tool for modern minimally invasive surgery. Most systems primarily capture a 2D projection of the 3D surgical field. Currently available 3D endoscopes can restore stereoscopic vision directly by projecting laterally shifted views of the operating field to each eye through 3D glasses. These tools provide surgeons with informative 3D visualizations, but they do not enable quantitative volumetric rendering of tissue. Therefore, advanced tools are desired to quantify tissue tomography for high precision microsurgery or medical robotics. Light-field imaging suggests itself as a promising solution to the challenge. The approach can capture both the spatial and angular information of optical signals, permitting the computational synthesis of the 3D volume of an object. In this work, we present GRIN lens array microendoscopy (GLAM), a single-shot, full-color, and quantitative 3D microendoscopy system. GLAM contains integrated fiber optics for illumination and a GRIN lens array to capture the reflected light field. The system exhibits a 3D resolution of ∼ 100 µm over an imaging depth of ∼ 22 mm and field of view up to 1 cm 2 . GLAM maintains a small form factor consistent with the clinically desirable design, making the system readily translatable to a clinical prototype.

Three-dimensional light-field microendoscopy with a GRIN lens array

: We demonstrate a simple, low-cost two-photon microscope design with both galvo-galvo and resonant-galvo scanning capabilities. We quantify and compare the signal-to-noise ratios and imaging speeds of the galvo-galvo and resonant-galvo scanning modes when used for murine neurovascular imaging. The two scanning modes perform as expected under shot-noise limited detection and are found to achieve comparable signal-to-noise ratios. Resonant-galvo scanning is capable of reaching desired signal-to-noise ratios using less acquisition time when higher excitation power can be used. Given equal excitation power and total pixel dwell time between the two methods, galvo-galvo scanning outperforms resonant-galvo scanning in image quality when detection deviates from being shot-noise limited.

Evaluation of resonant scanning as a high-speed imaging technique for two-photon imaging of cortical vasculature

: Mid-IR is a useful wavelength range for both science and military applications due to its low atmospheric attenuation and ability to be used for passive detection. However, many solutions for detecting light in this spectral region need to be operated at cryogenic temperatures as their required narrow bandgaps suffer from carrier recombination and band-to-band tunneling at room temperature leading to high dark currents. These problems can be alleviated by using a separate absorption, charge, and multiplication avalanche photodiode. We have recently demonstrated such a device with a 3-µm cutoff using Al 0.15 In 0.85 As 0.77 Sb 0.23 , as the absorber, grown on GaSb. Here we investigate Al 0.15 In 0.85 As 0.77 Sb 0.23 as a simple PIN homojunction and provide metrics to aid in future designs using this material. PL spectrum measurements indicate a bandgap of 2.94 µm at 300 K. External quantum efficiencies of 39% and 33% are achieved at 1.55 µm and 2 µm respectively. Between 180 K and 280 K the activation energy is ∼ 0.22 eV, roughly half the bandgap of Al 0.15 In 0.85 As 0.77 Sb 0.23 , indicating thermal generation is dominant.

Narrow bandgap Al 0.15 In 0.85 As 0.77 Sb 0.23 for mid-infrared photodetectors

The characteristics of inﬂuenza seasons varies substantially from year to year, posing challenges for public health preparation and response. Inﬂuenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the societal impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective inﬂuenza forecasting exercise, known as the FluSight challenge. A subset of participating teams has worked together to produce a collaborative multi-model ensemble, the FluSight Network ensemble. Uniting theoretical results from the forecasting literature with domain-speciﬁc forecasts from inﬂuenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize individual model weights and calibrate the ensemble via a beta transformation. We used the beta-transformed linear pool and the ﬁnite beta mixture model to produce ensemble forecasts retrospectively for the 2016/2017 to 2018/2019 inﬂuenza seasons in the U.S. We compared their performance to methods currently used in the FluSight challenge, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods’ modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve ensemble probabilistic scores in outbreak settings.

Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the u.s.

: Estimating the concentration of water constituents by optical remote sensing assumes absorption and scattering processes to be uniform over the observation depth. Using hyperspectral reflectance, we present a method to direct the retrieval of the backscattering coefficient ( b b ( λ )) from reflectance ( > 600 nm) towards wavebands where absorption by water dominates the reflectance curve. Two experiments demonstrate the impact of hyperspectral inversion in the selected band set. First, optical simulations show that the resulting distribution of b b ( λ ) is sensitive to particle mixing conditions, although a robust indicator of non-uniformity was not found for all scenarios of stratification. Second, in the absence of spectral backscattering profiles from in situ data sets, it is shown how substituting the median of b b ( λ ) into a near infra-red / red band ratio algorithm improved chlorophyll- a estimates (root mean square error 75.45 mg m − 3 became 44.13 mg m − 3 ). This approach also allows propagation of the uncertainty in b b estimates to water constituent concentrations.

Improved hyperspectral inversion of aquatic reflectance under non-uniform vertical mixing

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