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.

The use of multimode fibers offers advantages in the field of communication technology in terms of transferable information density and information security. For applications using physical layer security or mode division multiplexing, the complex transmission matrix provides valuable knowledge about the communication channel. To measure the transmission matrix, the individual modes of the multimode fiber are excited sequentially at the input and a mode decomposition is performed at the output. Mode decomposition is usually performed using digital holography, which requires a reference wave and leads to high efforts. To overcome these drawbacks, a neural network is proposed, which performs mode decomposition with intensity-only camera recordings of the multimode fiber facet. Due to the high computational complexity of the problem, this approach was usually limited to a number of 6 modes. In this work, it is shown for the first time that by using a DenseNet with 121 layers it is possible to break through the hurdle of 6 modes. The training process is based on synthetic data. The advancement is demonstrated by a mode decomposition with 10 modes experimentally. A quantitative comparison of the proposed method with digital holography shows very good agreement. In addition, it is shown that the network can perform mode decomposition on a subset of 10 modes of a 55-mode fiber, which also supports modes unknown to the neural network. The smart detection using a DenseNet opens new ways for the application of multimode fibers in optical communication networks for physical layer security.

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.

Multimode fibers (MMF) are promising candidates to increase the data rate while reducing the space required for optical fiber networks. However, their use is hampered by mode mixing and other effects, leading to speckled output patterns. This can be overcome by measuring the transmission matrix (TM) of a multimode fiber. In this contribution, a mode-selective excitation of complex amplitudes is performed with only one phase-only spatial light modulator. The light field propagating through the fiber is measured holographically and is analyzed by a rapid decomposition method. This technique requires a small amount of measurements N, which corresponds to the degree of freedom of the fiber. The TM determines the amplitude and phase relationships of the modes, which allows us to understand the mode scrambling processes in the MMF and can be used for mode division multiplexing.

Imaging-based flow measurement techniques, like particle image velocimetry (PIV), are vulnerable to time-varying distortions like refractive index inhomogeneities or fluctuating phase boundaries. Such distortions strongly increase the velocity error, as the position assignment of the tracer particles and the decrease of image contrast exhibit significant uncertainties. We demonstrate that wavefront shaping based on spatially distributed guide stars has the potential to significantly reduce the measurement uncertainty. Proof of concept experiments show an improvement by more than one order of magnitude. Possible applications for the wavefront shaping PIV range from measurements in jets and film flows to biomedical applications.

The generation and application of human stem-cell-derived functional neural circuits promises novel insights into neurodegenerative diseases. These networks are often studied using stem-cell derived random neural networks in vitro, with electrical stimulation and recording using multielectrode arrays. However, the impulse response function of networks is best obtained with spatiotemporally well-defined stimuli, which electrical stimulation does not provide. Optogenetics allows for the functional control of genetically altered cells with light stimuli at high spatiotemporal resolution. Current optogenetic investigations of neural networks are often conducted using full field illumination, potentially masking important functional information. This can be avoided using holographically shaped illumination. In this article, we present a digital holographic illumination setup with a spatial resolution of about 8 µm, which suffices for the stimulation of single neurons, and offers a temporal resolution of less than 0.6 ms. With this setup, we present preliminary single-cell stimulation recording of stem-cell derived induced human neurons in a random neural network. This will offer the opportunity for further studies on connectivity in such networks.