Bend translation in multimode fiber imaging

Structured light enhanced machine learning for fiber bend sensing

The intricate optical distortions that occur when light interacts with complex media, such as few- or multi-mode optical fiber, often appear random in origin and are a fundamental source of error for communication and sensing systems. We propose the use of orbital angular momentum (OAM) feature extraction to mitigate phase-noise and allow for the use of intermodal-coupling as an effective tool for fiber sensing. OAM feature extraction is achieved by passive all-optical OAM demultiplexing, and we demonstrate fiber bend tracking with 94.1% accuracy. Conversely, an accuracy of only 14% was achieved for determining the same bend positions when using a convolutional-neural-network trained with intensity measurements of the output of the fiber. Further, OAM feature extraction used 120 times less information for training compared to intensity image based measurements. This work indicates that structured light enhanced machine learning could be used in a wide range of future sensing technologies.

110 μm thin endo-microscope for deep-brain in vivo observations of neuronal connectivity, activity and blood flow dynamics

Light-based in-vivo brain imaging relies on light transport over large distances of highly scattering tissues. Scattering gradually reduces imaging contrast and resolution, making it difficult to reach structures at greater depths even with the use of multiphoton techniques. To reach deeper, minimally invasive endo-microscopy techniques have been established. These most commonly exploit graded-index rod lenses and enable a variety of modalities in head-fixed and freely moving animals. A recently proposed alternative is the use of holographic control of light transport through multimode optical fibres promising much less traumatic application and superior imaging performance. We present a 110 μm thin laser-scanning endo-microscope based on this prospect, enabling in-vivo volumetric imaging throughout the whole depth of the mouse brain. The instrument is equipped with multi-wavelength detection and three-dimensional random access options, and it performs at lateral resolution below 1 μm. We showcase various modes of its application through the observations of fluorescently labelled neurones, their processes and blood vessels. Finally, we demonstrate how to exploit the instrument to monitor calcium signalling of neurones and to measure blood flow velocity in individual vessels at high speeds.

Efficient dispersion modeling in optical multimode fiber

Dispersion remains an enduring challenge for the characterization of wavelength-dependent transmission through optical multimode fiber (MMF). Beyond a small spectral correlation width, a change in wavelength elicits a seemingly independent distribution of the transmitted field. Here we report on a parametric dispersion model that describes mode mixing in MMF as an exponential map and extends the concept of principal modes to describe the fiber's spectrally resolved transmission matrix (TM). We present computational methods to fit the model to measurements at only a few, judiciously selected, discrete wavelengths. We validate the model in various MMF and demonstrate an accurate estimation of the full TM across a broad spectral bandwidth, approaching the bandwidth of the best-performing principal modes, and exceeding the original spectral correlation width by more than two orders of magnitude. The model allows us to conveniently study the spectral behavior of principal modes, and obviates the need for dense spectral measurements, enabling highly efficient reconstruction of the multispectral TM of MMF.

Optical fiber specklegram sensor for multi-point curvature measurements

We present a multi-point curvature sensor based on optical fiber specklegram measurements. Apart from the current approaches, the proposed system uses an ordinary multimode fiber excited with visible light as a reflection-type probe. Besides, this method discretizes the waveguide into segments connected by joints and assumes sequential bend events, simplifying the specklegram referencing for correlation analyses and avoiding laborious deep learning processing. Sensor characterization yielded a linear response with ∼1.3^∘ resolution for single curvatures, whereas shape prediction experiments in the plane resulted in maximum errors of ∼3.5^∘ and ∼5.4mm for angular and linear positioning, respectively. Furthermore, exploratory tests indicated errors <2.3^∘ regarding probe curvatures in the space. This research introduces a feasible, straightforward alternative to the available shape sensors, enabling applications in medical probes and soft robotics.

Learning to sense three-dimensional shape deformation of a single multimode fiber

Optical fiber bending, deformation or shape sensing are important measurement technologies and have been widely deployed in various applications including healthcare, structural monitoring and robotics. However, existing optical fiber bending sensors require complex sensor structures and interrogation systems. Here, inspired by the recent renewed interest in information-rich multimode optical fibers, we show that the multimode fiber (MMF) output speckles contain the three-dimensional (3D) geometric shape information of the MMF itself. We demonstrate proof-of-concept 3D multi-point deformation sensing via a single multimode fiber by using k-nearest neighbor (KNN) machine learning algorithm, and achieve a classification accuracy close to 100%. Our results show that a single MMF based deformation sensor is excellent in terms of system simplicity, resolution and sensitivity, and can be a promising candidate in deformation monitoring or shape-sensing applications.

Confocal 3D reflectance imaging through multimode fiber without wavefront shaping

Imaging through optical multimode fiber (MMF) has the potential to enable hair-thin endoscopes that reduce the invasiveness of imaging deep inside tissues and organs. Active wavefront shaping and fluorescent labeling have recently been exploited to overcome modal scrambling and enable MMF imaging. Here, we present a computational approach that circumvents the need for active wavefront control and exogenous fluorophores. We demonstrate the reconstruction of depth-gated confocal images through MMF using a raster-scanned, focused input illumination at the fiber proximal end. We show the compatibility of this approach with quantitative phase, dark-field, and polarimetric imaging. Computational imaging through MMF opens a new pathway for minimally invasive imaging in medical diagnosis and biological investigations.

Tunable mode control through myriad-mode fibers

Multimode fibers are attractive for imaging, communication, computation, and energy delivery. Unfortunately, intermodal and polarization coupling precludes direct control of the delivered mode composition. We present a technique to tailor the mode composition at the output of a multimode fiber with thousands of modes, which we refer to as myriad-mode fiber, using its experimentally measured transmission matrix. While precise mode control has been demonstrated in typical multimode fibers with up to 210 modes, the method proposed here is particularly useful for high mode number fibers, such as when the number of modes is comparable to the number of modes of the wavefront shaping spatial light modulator. To illustrate the technique, we select different subsets of modes to create focal spots at the output of a fiber with 7140 modes. Importantly, we define efficiency and fidelity metrics to evaluate the mode control and demonstrate the relationship between efficiency, fidelity, and the spatial location of the spots across the distal fiber cross-section.

Comparison of round- and square-core fibers for sensing, imaging, and spectroscopy

Multimode fibers (MMFs) show great promise as miniature probes for sensing, imaging, and spectroscopy applications. Different parameters of the fibers, such as numerical aperture, refractive index profile and length, have been already optimized for better performance. Here we investigate the role of the core shape, in particular for wavefront shaping applications where a focus is formed at the output of the MMF. We demonstrate that in contrast to a conventional round-core MMF, a square-core design does not suffer from focus aberrations. Moreover, we find that how the interference pattern behind a square-core fiber decorrelates with the input frequency is largely independent of the input light coupling. Finally, we demonstrate that a square core shape provides an on-average uniform distribution of the output intensity, free from the input-output correlations seen in round fibers, showing great promise for imaging and spectroscopy applications.

Deconvolution for multimode fiber imaging: modeling of spatially variant PSF

Focusing light through a step-index multimode optical fiber (MMF) using wavefront control enables minimally-invasive endoscopy of biological tissue. The point spread function (PSF) of such an imaging system is spatially variant, and this variation limits compensation for blurring using most deconvolution algorithms as they require a uniform PSF. However, modeling the spatially variant PSF into a series of spatially invariant PSFs re-opens the possibility of deconvolution. To achieve this we developed svmPSF: an open-source Java-based framework compatible with ImageJ. The approach takes a series of point response measurements across the field-of-view (FOV) and applies principal component analysis to the measurements' co-variance matrix to generate a PSF model. By combining the svmPSF output with a modified Richardson-Lucy deconvolution algorithm, we were able to deblur and regularize fluorescence images of beads and live neurons acquired with a MMF, and thus effectively increasing the FOV.

Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift

Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wavefront propagates along its length is highly sensitive to wavelength changes. We use a tunable laser to implement a wavelength drift with a controlled bandwidth, aiming to estimate the DNN’s performance in different cases and identify the limitations. We find that when the DNNs are trained with a dataset which includes the noise induced by wavelength changes, successful classification of a speckle pattern can be performed even for a large wavelength bandwidth drift. A single training step is found to be sufficient for high classification accuracy, removing the need for time-consuming recalibration at each wavelength.

Focusing light in biological tissue through a multimode optical fiber: refractive index matching

Controlling light propagation through a step-index multimode optical fiber (MMF) has several important applications, including biological imaging. However, little consideration has been given to the coupling of fiber and tissue optics. In this Letter, we characterized the effects of tissue-induced light distortions, in particular those arising from a mismatch in the refractive index of the pre-imaging calibration and biological media. By performing the calibration in a medium matching the refractive index of the brain, optimal focusing ability was achieved, as well as a gain in focus uniformity within the field-of-view. These changes in illumination resulted in a 30% improvement in spatial resolution and intensity in fluorescence images of beads and live brain tissue. Beyond refractive index matching, our results demonstrate that sample-induced aberrations can severely deteriorate images from MMF-based systems.

Robust compressive multimode fiber imaging against bending with enhanced depth of field

Imaging through single multimode fiber prevails its counterpart using single mode fiber bundle on spatial resolution limit and minimum radius. Current multimode fiber imaging suffers from fussy calibration, which can be reduced by recent developed compressive sensing scheme [ [L. V. Amitonova, Opt. Lett. 43, 5427 (2018)]. Experiments demonstrate improvement on depth of field by more than three orders of magnitude, together with robustness against macro fiber bending, which is vital to endoscopic applications.

Characterization of an imaging multimode optical fiber using a digital micro-mirror device based single-beam system

This work demonstrates experimental approaches to characterize a single multimode fiber imaging system without a reference beam. Spatial light modulation is performed with a digital micro-mirror device that enables high-speed binary amplitude modulation. Intensity-only images are recorded by the camera and processed by a Bayesian inference based algorithm to retrieve the phase of the output optical field as well as the transmission matrix of the fiber. The calculated transmission matrix is validated by three standards: prediction accuracy, transmission imaging, and focus generation. Also, it is found that information on mode count and eigenchannels can be extracted from the transmission matrix by singular value decomposition. This paves the way for a more compact and cheaper single multimode fiber imaging system for many demanding imaging tasks.

Self-calibration of lensless holographic endoscope using programmable guide stars

Coherent fiber bundle (CFB)-based endoscopes enable optical keyhole access in applications such as biophotonics. In conjunction with objective lenses, CFBs allow imaging of intensity patterns. In contrast, digital optical phase conjugation enables lensless holographic endoscopes for the generation of pixelation-free arbitrary light patterns. For real-world applications, however, this requires a non-invasive in situ calibration of the complex optical transfer function of the CFB with only single-sided access. We show that after an initial calibration in a forward direction, a differential phase measurement of the back-reflected light allows for tracking and compensating of bending-induced phase distortions. Furthermore, we present a novel in situ calibration procedure based on a programmable guide star, which requires access to only one side of the fiber.

Robustness of Light-Transport Processes to Bending Deformations in Graded-Index Multimode Waveguides

Light transport through a multimode optical waveguide undergoes changes when subjected to bending deformations. We show that optical waveguides with a perfectly parabolic refractive index profile are almost immune to bending, conserving the structure of propagation-invariant modes. Moreover, we show that changes to the transmission matrix of parabolic-index fibers due to bending can be expressed with only two free parameters, regardless of how complex a particular deformation is. We provide detailed analysis of experimentally measured transmission matrices of a commercially available graded-index fiber as well as a gradient-index rod lens featuring a very faithful parabolic refractive index profile. Although parabolic-index fibers with a sufficiently precise refractive index profile are not within our reach, we show that imaging performance with standard commercially available graded-index fibers is significantly less influenced by bending deformations than step-index types under the same conditions. Our work thus predicts that the availability of ultraprecise parabolic-index fibers will make endoscopic applications with flexible probes feasible and free from extremely elaborate computational challenges.

Bending-induced inter-core group delays in multicore fibers

We examine the impact of fiber bends on ultrashort pulse propagation in a 169-core multicore fiber (MCF) by numerical simulations and experimental measurements. We show that an L-shaped bend (where only one end of the MCF is fixed) induces significant changes in group delays that are a function of core position but linear along the bending axis with a slope directly proportional to the bending angle. For U- and S-shaped bends (where both ends of the MCF are fixed) the induced refractive index and group delay changes are much smaller than the residual, intrinsic inter-core group delay differences of the unbent MCF. We further show that when used for point-scanning lensless endoscopy with ultrashort pulse excitation, bend-induced group delays in the MCF degrade the point-spread function due to spatiotemporal coupling. Our results show that bend-induced effects in MCFs can be parametrized with only two parameters: the angle of the bend axis and the amplitude of the bend. This remains valid for bend amplitudes up to at least 200 degrees.