Computational optical imaging with a photonic lantern

Multimode fiber endoscopes for computational brain imaging

Advances in imaging tools have always been a pivotal driver for new discoveries in neuroscience. An ability to visualize neurons and subcellular structures deep within the brain of a freely behaving animal is integral to our understanding of the relationship between neural activity and higher cognitive functions. However, fast high-resolution imaging is limited to sub-surface brain regions and generally requires head fixation of the animal under the microscope. Developing new approaches to address these challenges is critical. The last decades have seen rapid progress in minimally invasive endo-microscopy techniques based on bare optical fibers. A single multimode fiber can be used to penetrate deep into the brain without causing significant damage to the overlying structures and provide high-resolution imaging. Here, we discuss how the full potential of high-speed super-resolution fiber endoscopy can be realized by a holistic approach that combines fiber optics, light shaping, and advanced computational algorithms. The recent progress opens up new avenues for minimally invasive deep brain studies in freely behaving mice.

Compact and efficient photonic lanterns through multi-stage tapering

Photonic lanterns (PLs) have been recently used in mode-division multiplexed systems with a low insertion loss, a low mode-dependent loss (MDL), and a wide bandwidth. However, the cross talk (XT) performance of the PLs requires further enhancement within a short taper length. In this Letter, a multi-stage cascaded scheme for short PLs is proposed to further improve the performance on losses and XT. The XT of the optimized 6-mode 3-stage PL is below -23.4 dB, while the total length is only 6 cm. To the best of our knowledge, this is the first quantitative optimization of a 3-stage tapered PL, resulting in a compact structure and excellent performance. Furthermore, we experimentally validate the feasibility of the 3-stage tapering process.

Mode division multiplexing reconstructive spectrometer with an all-fiber photonics lantern

This study presents a high-accuracy, all-fiber mode division multiplexing (MDM) reconstructive spectrometer (RS). The MDM was achieved by utilizing a custom-designed 3 × 1 mode-selective photonics lantern to launch distinct spatial modes into the multimode fiber (MMF). This facilitated the information transmission by increasing light scattering processes, thereby encoding the optical spectra more comprehensively into speckle patterns. Spectral resolution of 2 pm and the recovery of 2000 spectral channels were accomplished. Compared to methods employing single-mode excitation and two-mode excitation, the three-mode excitation method reduced the recovered error by 88% and 50% respectively. A resolution enhancement approach based on alternating mode modulation was proposed, reaching the MMF limit for the 3 dB bandwidth of the spectral correlation function. The proof-of-concept study can be further extended to encompass diverse programmable mode excitations. It is not only succinct and highly efficient but also well-suited for a variety of high-accuracy, high-resolution spectral measurement scenarios.

A primal-dual data-driven method for computational optical imaging with a photonic lantern

Optical fibers aim to image in vivo biological processes. In this context, high spatial resolution and stability to fiber movements are key to enable decision-making processes (e.g. for microendoscopy). Recently, a single-pixel imaging technique based on a multicore fiber photonic lantern has been designed, named computational optical imaging using a lantern (COIL). A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy. In this work, we develop a data-driven approach for COIL. We replace the sparsity prior in the proximal algorithm by a learned denoiser, leading to a plug-and-play (PnP) algorithm. The resulting PnP method, based on a proximal primal-dual algorithm, enables to solve the Morozov formulation of the inverse problem. We use recent results in learning theory to train a network with desirable Lipschitz properties, and we show that the resulting primal-dual PnP algorithm converges to a solution to a monotone inclusion problem. Our simulations highlight that the proposed data-driven approach improves the reconstruction quality over variational SARA-COIL method on both simulated and real data.

Swept-source multimode fiber imaging

High-resolution compressive imaging via a flexible multimode fiber is demonstrated using a swept-laser source and wavelength dependent speckle illumination. An in-house built swept-source allowing for independent control of bandwidth and scanning range is used to explore and demonstrate a mechanically scan-free approach for high-resolution imaging through an ultrathin and flexible fiber probe. The computational image reconstruction is shown by utilizing a narrow sweeping bandwidth of [Formula: see text] nm while acquisition time is decreased by 95% compared to conventional raster scanning endoscopy. Demonstrated narrow-band illumination in the visible spectrum is vital for the detection of fluorescence biomarkers in neuroimaging applications. The proposed approach yields device simplicity and flexibility for minimally invasive endoscopy.

Sub-diffraction computational imaging via a flexible multicore-multimode fiber

An ultra-thin multimode fiber is an ideal platform for minimally invasive microscopy with the advantages of a high density of modes, high spatial resolution, and a compact size. In practical applications, the probe needs to be long and flexible, which unfortunately destroys the imaging capabilities of a multimode fiber. In this work, we propose and experimentally demonstrate sub-diffraction imaging through a flexible probe based on a unique multicore-multimode fiber. A multicore part consists of 120 Fermat's spiral distributed single-mode cores. Each of the cores offers stable light delivery to the multimode part, which provides optimal structured light illumination for sub-diffraction imaging. As a result, perturbation-resilient fast sub-diffraction fiber imaging by computational compressive sensing is demonstrated.

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.

Recent Advances in Biomedical Photonic Sensors: A Focus on Optical-Fibre-Based Sensing

In this invited review, we provide an overview of the recent advances in biomedical photonic sensors within the last five years. This review is focused on works using optical-fibre technology, employing diverse optical fibres, sensing techniques, and configurations applied in several medical fields. We identified technical innovations and advancements with increased implementations of optical-fibre sensors, multiparameter sensors, and control systems in real applications. Examples of outstanding optical-fibre sensor performances for physical and biochemical parameters are covered, including diverse sensing strategies and fibre-optical probes for integration into medical instruments such as catheters, needles, or endoscopes.

Photonic lantern tip/tilt detector for adaptive optics systems

In this work, we demonstrate a four-core multicore fiber photonic lantern tip/tilt wavefront sensor. To diagnose the low-order Zernike aberrations, we exploit the ability of the photonic lantern to encode the characteristics of a complex incoming beam at the multimode facet of the sensor to intensity distributions at the multicore fiber output. Here, we provide a comprehensive numerical analysis capable of predicting the performance of fabricated devices and experimentally demonstrate the concept. Two receiver architectures are implemented to discern tip/tilt information by (i) imaging the four-core fiber facet on a 2D detector and (ii) direct power measurement of the single mode outputs using a multicore fiber multiplexer and photodetectors. For both receiver schemes, an angular detection window of ∼0.4^∘ at 1064 nm can be achieved. Our results are expected to further facilitate the development of intensity-based fiber wavefront sensors for adaptive optics systems.

Memory effect assisted imaging through multimode optical fibres

When light propagates through opaque material, the spatial information it holds becomes scrambled, but not necessarily lost. Two classes of techniques have emerged to recover this information: methods relying on optical memory effects, and transmission matrix (TM) approaches. Here we develop a general framework describing the nature of memory effects in structures of arbitrary geometry. We show how this framework, when combined with wavefront shaping driven by feedback from a guide-star, enables estimation of the TM of any such system. This highlights that guide-star assisted imaging is possible regardless of the type of memory effect a scatterer exhibits. We apply this concept to multimode fibres (MMFs) and identify a 'quasi-radial' memory effect. This allows the TM of an MMF to be approximated from only one end - an important step for micro-endoscopy. Our work broadens the applications of memory effects to a range of novel imaging and optical communication scenarios.