Medical microrobots (MRs) have been demonstrated for a variety of non-invasive biomedical applications, such as tissue engineering, drug delivery, and assisted fertilization, among others. However, most of these demonstrations have been carried out in in vitro settings and under optical microscopy, being significantly different from the clinical practice. Thus, medical imaging techniques are required for localizing and tracking such tiny therapeutic machines when used in medical-relevant applications. This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field. Moreover, the physics and the working principle behind each analyzed imaging strategy, the spatiotemporal resolution, and the penetration depth are thoroughly discussed. The paper deals with the suitability of each imaging technique for tracking single or swarms of MRs and discusses the scenarios where contrast or imaging agent's inclusion is required, either to absorb, emit, or reflect a determined physical signal detected by an external system. Finally, the review highlights the existing challenges and perspective solutions which could be promising for future in vivo applications.

Medical micromotors have the potential to lead to a paradigm shift in future biomedicine, as they may perform active drug delivery, microsurgery, tissue engineering, or assisted fertilization in a minimally invasive manner. However, the translation to clinical treatment is challenging, as many applications of single or few micromotors require real-time tracking and control at high spatiotemporal resolution in deep tissue. Although optical techniques are a popular choice for this task, absorption and strong light scattering lead to a pronounced decrease of the signal-to-noise ratio with increasing penetration depth. Here, a highly reflective micromotor is introduced which reflects more than tenfold the light intensity of simple gold particles and can be precisely navigated by external magnetic fields. A customized optical IR imaging setup and an image correlation technique are implemented to track single micromotors in real-time and label-free underneath phantom and ex vivo mouse skull tissues. As a potential application, the micromotors speed is recorded when moving through different viscous fluids to determine the viscosity of diverse physiological fluids toward remote cardiovascular disease diagnosis. Moreover, the micromotors are loaded with a model drug to demonstrate their cargotransport capability. The proposed reflective micromotor is suitable as theranostic tool for sub-skin or organ-on-a-chip applications.

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.

In this paper we analyze the capability of adaptive lenses to replace mechanical axial scanning in confocal microscopy. The adaptive approach promises to achieve high scan rates in a rather simple implementation. This may open up new applications in biomedical imaging or surface analysis in micro- and nanoelectronics, where currently the axial scan rates and the flexibility at the scan process are the limiting factors. The results show that fast and adaptive axial scanning is possible using electrically tunable lenses but the performance degrades during the scan. This is due to defocus and spherical aberrations introduced to the system by tuning of the adaptive lens. These detune the observation plane away from the best focus which strongly deteriorates the axial resolution by a factor of ~2.4. Introducing balancing aberrations allows addressing these influences. The presented approach is based on the employment of a second adaptive lens, located in the detection path. It enables shifting the observation plane back to the best focus position and thus creating axial scans with homogeneous axial resolution. We present simulated and experimental proof-of-principle results.

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Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

Few-mode fibers (FMF) are promising for advancements in transmission capacity in classical and quantum communications. However, the inherent modal crosstalk limits the practical application of FMF. One reliable way to overcome this obstacle is the measurement of the complex transmission matrix (TM) describing the light propagation behavior of fiber. The TM can be obtained by performing mode decomposition (MD) of the spatial modes at the output of the fiber. MD techniques require retrieval of both amplitude and phase components of the detected light field which is commonly done by using holography. However, the provision of a reference wave is highly unfavorable for the implementation of a holography-based MD in communication technology, especially for long fibers. Using deep neural networks processing intensity-only images, this drawback can be overcome. We introduce the Mode Transformer Network which is able to perform MD on 23 modes and has been trained offline using synthetic data. Experimentally, we demonstrate, for the first time, not only the measurement of complex TM of an FMF, but also the inversion of the TM by a deep learning-based MD method. For mode transmission, we achieve an average fidelity of 97%. The short duration of the determination of TM allows overcoming time-varying effects due to e.g. mechanical stress or temperature fluctuations. The proposed reference-less calibration is promising for fiber communication with classical light and single photons such as at quantum key distribution.

Multimode fibers are attractive for a variety of applications such as communication engineering and biophotonics. However, a major hurdle for the optical transmission through multimode fibers is the inherent mode mixing. Although an image transmission was successfully accomplished using wavefront shaping, the image information was not transmitted individually for each of the independent pixels. We demonstrate a transmission of independent signals using individually shaped wavefronts employing a single segmented spatial light modulator for optical phase conjugation regarding each light signal. Our findings pave the way towards transferring independent signals through strongly scattering media.