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Register a new userTowards smart optical focusing: Deep learning-empowered wavefront shaping in nonstationary scattering media
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Yunqi Luo
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Optical focusing at depths in tissue is the Holy Grail of biomedical optics that may bring revolutionary advancement to the field. Wavefront shaping is a widely accepted approach to solve this problem, but most implementations thus far have only operated with stationary media which, however, are scarcely existent in practice. In this article, we propose to apply a deep convolutional neural network named as ReFocusing-Optical-Transformation-Net (RFOTNet), which is a Multi-input Single-output network, to tackle the grand challenge of light focusing in nonstationary scattering media. As known, deep convolutional neural networks are intrinsically powerful to solve inverse scattering problems without complicated computation. Considering the optical speckles of the medium before and after moderate perturbations are correlated, an optical focus can be rapidly recovered based on fine-tuning of pre-trained neural networks, significantly reducing the time and computational cost in refocusing. The feasibility is validated experimentally in this work. The proposed deep learning-empowered wavefront shaping framework has great potentials in facilitating optimal optical focusing and imaging in deep and dynamic tissue.
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Non-invasively focusing light into strongly scattering media, such as biological tissue, is highly desirable but challenging. Recently, wavefront shaping technologies guided by ultrasonic encoding or photoacoustic sensing have been developed to address this limitation. So far, these methods provide only acoustic diffraction-limited optical focusing. Here, we introduce nonlinear photoacoustic wavefront shaping (PAWS), which achieves optical diffraction-limited (i.e. single-speckle-grain) focusing in scattering media. We develop an efficient dual-pulse excitation approach to generate strong nonlinear photoacoustic (PA) signals based on the Grueneisen memory effect. These nonlinear PA signals are used as feedback to guide iterative wavefront optimization. By maximizing the amplitude of the nonlinear PA signal, light is effectively focused to a single optical speckle grain. Experimental results demonstrate a clear optical focus on the scale of 5-7 micrometers, which is ~10 times smaller than the acoustic focus in linear dimension, with an enhancement factor of ~6000 in peak fluence. This technology has the potential to provide highly confined strong optical focus deep in tissue for microsurgery of Parkinsons disease and epilepsy or single-neuron imaging and optogenetic activation.
We grow accustomed to the notion that optical susceptibilities can be treated as a local property of a medium. In the context of nonlinear optics, both Kerr and Raman processes are considered local, meaning that optical fields at one location do not produce a nonlinear response at distinct locations in space. This is because the electronic and phononic disturbances produced within the material are confined to a region that is smaller than an optical wavelength. By comparison, Brillouin interactions can result in a highly nonlocal nonlinear response, as the elastic waves generated through the Brillouin process can occupy a region in space much larger than an optical wavelength. The nonlocality of these interactions can be exploited to engineer new types of processes, where highly delocalized phonon modes serve as an engineerable channel that mediates scattering processes between light waves propagating in distinct optical waveguides. These types of nonlocal optomechanical responses have been recently demonstrated as the basis for information transduction, however the nontrivial dynamics of such systems has yet to be explored. In this work, we show that the third-order nonlinear process resulting from spatially extended Brillouin-active phonon modes involves mixing products from spatially separated, optically decoupled waveguides, yielding a nonlocal joint-susceptibility. We further explore the coupling of multiple acoustic modes and show that multi-mode acoustic interference enables a tailorable nonlocal-nonlinear susceptibility, exhibiting a multi-pole frequency response.
Optical focusing through/inside scattering media, like multimode fiber and biological tissues, has significant impact in biomedicine yet considered challenging due to strong scattering nature of light. Previously, promising progress has been made, benefiting from the iterative optical wavefront shaping, with which deep-tissue high-resolution optical focusing becomes possible. Most of iterative algorithms can overcome noise perturbations but fail to effectively adapt beyond the noise, e.g. sudden strong perturbations. Re-optimizations are usually needed for significant decorrelated medium since these algorithms heavily rely on the optimization in the previous iterations. Such ineffectiveness is probably due to the absence of a metric that can gauge the deviation of the instant wavefront from the optimum compensation based on the concurrently measured optical focusing. In this study, a square rule of binary-amplitude modulation, directly relating the measured focusing performance with the error in the optimized wavefront, is theoretically proved and experimentally validated. With this simple rule, it is feasible to quantify how many pixels on the spatial light modulator incorrectly modulate the wavefront for the instant status of the medium or the whole system. As an example of application, we propose a novel algorithm, dynamic mutation algorithm, with high adaptability against perturbations by probing how far the optimization has gone toward the theoretically optimum. The diminished focus of scattered light can be effectively recovered when perturbations to the medium cause significant drop of the focusing performance, which no existing algorithms can achieve due to their inherent strong dependence on previous optimizations. With further improvement, this study may boost or inspire many applications, like high-resolution imaging and stimulation, in instable scattering environments.
A novel technique is presented for realising programmable silicon photonic circuits. Once the proposed photonic circuit is programmed, its routing is retained without the need for additional power consumption. This technology enables a uniform multi-purpose design of photonic chips for a range of different applications and performance requirements, as it can be programmed for each specific application after chip fabrication. Therefore the cost per chip can be dramatically reduced because of the increase in production volume, and rapid prototyping of new photonic circuits is enabled. Essential building blocks for programmable circuits, erasable directional couplers (DCs) were designed and fabricated, utilising ion implanted waveguides. We demonstrate permanent switching between the drop port and through port of the DCs using a localised post-fabrication laser annealing process. Proof-of-principle demonstrators in the form of generic 1X4 and 2X2 programmable switching circuits were then fabricated and subsequently programmed, to define their function.
Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here we demonstrate the ability of deep neural networks to classify numerically-generated, noisy Laguerre-Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity. The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum. We also present results that show the strength of deep neural networks in the classification of experimental superpositions of Laguerre-Gauss modes when the networks are trained solely using simulated images. It is anticipated that these results will allow for an enhancement of current optical communications technologies.
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