Toward peripheral nerve mechanical characterization using Brillouin imaging spectroscopy

SIGNIFICANCE: Peripheral nerves are viscoelastic tissues with unique elastic characteristics. Imaging of peripheral nerve elasticity is important in medicine, particularly in the context of nerve injury and repair. Elasticity imaging techniques provide information about the mechanical properties of peripheral nerves, which can be useful in identifying areas of nerve damage or compression, as well as assessing the success of nerve repair procedures. AIM: We aim to assess the feasibility of Brillouin microspectroscopy for peripheral nerve imaging of elasticity, with the ultimate goal of developing a new diagnostic tool for peripheral nerve injury APPROACH: Viscoelastic properties of the peripheral nerve were evaluated with Brillouin imaging spectroscopy. RESULTS: An external stress exerted on the fixed nerve resulted in a Brillouin shift. Quantification of the shift enabled correlation of the Brillouin parameters with nerve elastic properties. CONCLUSIONS: Brillouin microscopy provides sufficient sensitivity to assess viscoelastic properties of peripheral nerves

Pure electrical, highly-efficient and sidelobe free coherent Raman spectroscopy using acousto-optics tunable filter (AOTF)

Fast and sensitive Raman spectroscopy measurements are imperative for a large number of applications in biomedical imaging, remote sensing and material characterization. Stimulated Raman spectroscopy offers a substantial improvement in the signal-to-noise ratio but is often limited to a discrete number of wavelengths. In this report, by introducing an electronically-tunable acousto-optical filter as a wavelength selector, a novel approach to a broadband stimulated Raman spectroscopy is demonstrated. The corresponding Raman shift covers the spectral range from 600 cm(−1) to 4500 cm(−1), sufficient for probing most vibrational Raman transitions. We validated the use of the new instrumentation to both coherent anti-Stokes scattering (CARS) and stimulated Raman scattering (SRS) spectroscopies

Assessing performance of modern Brillouin spectrometers

Brillouin spectroscopy and imaging has experienced a renaissance in recent years seeing vast improvements in methodology and increasing number of applications. With this resurgence has come the development of new spontaneous Brillouin instruments that often tout superior performance compared to established conventional systems such as tandem Fabry-Perot interferometers (TFPI). The performance of these new systems cannot always be thoroughly examined beyond the scope of the intended application, as applications often take precedence in reports. We therefore present evaluation of three modern Brillouin spectrometers: two VIPA-based spectrometers with wavelength-specific notch filters, and one scanning 6-pass TFPI. Performance analysis is presented along with a discussion about the dependence of measurements on excitation laser source and the various susceptibilities of each system

Optical neural network architecture for deep learning with the temporal synthetic dimension

The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension. The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network, and can be linearly transformed with splitters and delay lines, including the phase modulators, when pulses circulate inside the network. Such linear transformation can be arbitrarily controlled by applied modulation phases, which serve as the building block of the neural network together with a nonlinear component for pulses. We validate the functionality of the proposed optical neural network for the deep learning purpose with examples handwritten digit recognition and optical pulse train distribution classification problems. This proof of principle computational work explores the new concept of developing a photonics-based machine learning in a single ring network using synthetic dimensions, which allows flexibility and easiness of reconfiguration with complex functionality in achieving desired optical tasks

Sub-cycle optical control of current in a semiconductor: from the multiphoton to the tunneling regime

Nonlinear interactions between ultrashort optical waveforms and solids can be used to induce and steer electric current on a femtosecond (fs) timescale, holding promise for electronic signal processing at PHz frequencies [Nature 493, 70 (2013)]. So far, this approach has been limited to insulators, requiring extremely strong peak electric fields and intensities. Here, we show all-optical generation and control of directly measurable electric current in a semiconductor relevant for high-speed and high-power (opto)electronics, gallium nitride (GaN), within an optical cycle and on a timescale shorter than 2 fs, at intensities at least an order of magnitude lower than those required for dielectrics. Our approach opens the door to PHz electronics and metrology, applicable to low-power (non-amplified) laser pulses, and may lead to future applications in semiconductor and photonic integrated circuit technologies