Computational multi-depth single-photon imaging

We present an imaging framework that is able to accurately reconstruct multiple depths at individual pixels from single-photon observations. Our active imaging method models the single-photon detection statistics from multiple reflectors within a pixel, and it also exploits the fact that a multi-depth profile at each pixel can be expressed as a sparse signal. We interpret the multi-depth reconstruction problem as a sparse deconvolution problem using single-photon observations, create a convex problem through discretization and relaxation, and use a modified iterative shrinkage-thresholding algorithm to efficiently solve for the optimal multi-depth solution. We experimentally demonstrate that the proposed framework is able to accurately reconstruct the depth features of an object that is behind a partially-reflecting scatterer and 4 m away from the imager with root mean-square error of 11 cm, using only 19 signal photon detections per pixel in the presence of moderate background light. In terms of root mean-square error, this is a factor of 4.2 improvement over the conventional method of Gaussian-mixture fitting for multi-depth recovery.This material is based upon work supported in part by a Samsung Scholarship, the US National Science Foundation under Grant No. 1422034, and the MIT Lincoln Laboratory Advanced Concepts Committee. We thank Dheera Venkatraman for his assistance with the experiments. (Samsung Scholarship; 1422034 - US National Science Foundation; MIT Lincoln Laboratory Advanced Concepts Committee)Accepted manuscrip

Varifocal diffractive lenses for multi-depth microscope imaging

Flat optical elements enable the realization of ultra-thin devices able to either reproduce or overcome the functionalities of standard bulky components. The fabrication of these elements involves the structuration of material surfaces on the light wavelength scale, whose geometry has to be carefully designed to achieve the desired optical functionality. In addition to the limits imposed by lithographic design-performance compromises, their optical behavior cannot be accurately tuned afterward, making them difficult to integrate in dynamic optical systems. Here we show the realization of fully reconfigurable flat varifocal diffractive lens, which can be in-place realized, erased and reshaped directly on the surface of an azopolymer film by an all-optical holographic process. Integrating the lens in the same optical system used as standard refractive microscope, results in a hybrid microscope capable of multi-depth object imaging. Our approach demonstrates that reshapable flat optics can be a valid choice to integrate, or even substitute, modern optical systems for advanced functionalities

Femtosecond laser machining of multi-depth microchannel networks onto silicon

Direct writing of multi-depth microchannel branching networks into a silicon wafer with femtosecond pulses at 200 kHz is reported. The silicon wafer with the microchannels is used as the mold for rapid prototyping of microchannels on polydimethylsiloxane. The branching network is designed to serve as a gas exchanger for use in artificial lungs and bifurcates according to Murray's law. In the development of such micro-fluidic structures, processing speed, machining range with quality surface, and precision are significant considerations. The scan speed is found to be a key parameter to reduce the processing time, to expand the machining range, and to improve the surface quality. By fabricating a multi-depth branching network as an example, the utilization of femtosecond pulses in the development of microfluidic devices is demonstrated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90801/1/0960-1317_21_4_045027.pd

Bayesian Measurement Error Correction in Structured Additive Distributional Regression with an Application to the Analysis of Sensor Data on Soil-Plant Variability

The flexibility of the Bayesian approach to account for covariates with measurement error is combined with semiparametric regression models for a class of continuous, discrete and mixed univariate response distributions with potentially all parameters depending on a structured additive predictor. Markov chain Monte Carlo enables a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of unobserved error-free covariate values. We allow for very general measurement errors, including correlated replicates with heterogeneous variances. The proposal is first assessed by a simulation trial, then it is applied to the assessment of a soil-plant relationship crucial for implementing efficient agricultural management practices. Observations on multi-depth soil information forage ground-cover for a seven hectares Alfalfa stand in South Italy were obtained using sensors with very refined spatial resolution. Estimating a functional relation between ground-cover and soil with these data involves addressing issues linked to the spatial and temporal misalignment and the large data size. We propose a preliminary spatial interpolation on a lattice covering the field and subsequent analysis by a structured additive distributional regression model accounting for measurement error in the soil covariate. Results are interpreted and commented in connection to possible Alfalfa management strategies

Coupling between gamma-band power and cerebral blood volume during recurrent acute neocortical seizures

Characterization of neural and hemodynamic biomarkers of epileptic activity that can be measured using non-invasive techniques is fundamental to the accurate identification of the epileptogenic zone (EZ) in the clinical setting. Recently, oscillations at gamma-band frequencies and above (>30 Hz) have been suggested to provide valuable localizing information of the EZ and track cortical activation associated with epileptogenic processes. Although a tight coupling between gamma-band activity and hemodynamic-based signals has been consistently demonstrated in non-pathological conditions, very little is known about whether such a relationship is maintained in epilepsy and the laminar etiology of these signals. Confirmation of this relationship may elucidate the underpinnings of perfusion-based signals in epilepsy and the potential value of localizing the EZ using hemodynamic correlates of pathological rhythms. Here, we use concurrent multi-depth electrophysiology and 2-dimensional optical imaging spectroscopy to examine the coupling between multi-band neural activity and cerebral blood volume (CBV) during recurrent acute focal neocortical seizures in the urethane-anesthetized rat. We show a powerful correlation between gamma-band power (25-90 Hz) and CBV across cortical laminae, in particular layer 5, and a close association between gamma measures and multi-unit activity (MUA). Our findings provide insights into the laminar electrophysiological basis of perfusion-based imaging signals in the epileptic state and may have implications for further research using non-invasive multi-modal techniques to localize epileptogenic tissue

Development of multi-depth probing 3D microelectrode array to record electrophysiological activity within neural cultures

Microelectrode arrays (MEAs) play a crucial role in investigating the electrophysiological activities of neuronal populations. Although two-dimensional neuronal cell cultures have predominated in neurophysiology in monitoring in-vitro the electrophysiological activity, recent research shifted toward culture using three-dimensional (3D) neuronal network structures for developing more sophisticated and realistic neuronal models. Nevertheless, many challenges remain in the electrophysiological analysis of 3D neuron cultures, among them the development of robust platforms for investigating the electrophysiological signal at multiple depths of the 3D neurons’ networks. While various 3D MEAs have been developed to probe specific depths within the layered nervous system, the fabrication of microelectrodes with different heights, capable of probing neural activity from the surface as well as from the different layers within the neural construct, remains challenging. This study presents a novel 3D MEA with microelectrodes of different heights, realized through a multi-stage mold-assisted electrodeposition process. Our pioneering platform allows meticulous control over the height of individual microelectrodes as well as the array topology, paving the way for the fabrication of 3D MEAs consisting of electrodes with multiple heights that could be tailored for specific applications and experiments. The device performance was characterized by measuring electrochemical impedance, and noise, and capturing spontaneous electrophysiological activity from neurospheroids derived from human induced pluripotent stem cells. These evaluations unequivocally validated the significant potential of our innovative multi-height 3D MEA as an avant-garde platform for in vitro 3D neuronal studies