The mechanical properties of biological tissues are increasingly recognized as important factors in developmental and pathological processes. Most existing mechanical measurement techniques either necessitate destruction of the tissue for access or provide insufficient spatial resolution. Here, we show for the first time to our knowledge a systematic application of confocal Brillouin microscopy to quantitatively map the mechanical properties of spinal cord tissues during biologically relevant processes in a contact-free and nondestructive manner. Living zebrafish larvae were mechanically imaged in all anatomical planes during development and after spinal cord injury. These experiments revealed that Brillouin microscopy is capable of detecting the mechanical properties of distinct anatomical structures without interfering with the animal's natural development. The Brillouin shift within the spinal cord remained comparable during development and transiently decreased during the repair processes after spinal cord transection. By taking into account the refractive index distribution, we explicitly determined the apparent longitudinal modulus and viscosity of different larval zebrafish tissues. Importantly, mechanical properties differed between tissues in situ and in excised slices. The presented work constitutes the first step toward an in vivo assessment of spinal cord tissue mechanics during regeneration, provides a methodical basis to identify key determinants of mechanical tissue properties, and allows us to test their relative importance in combination with biochemical and genetic factors during developmental and regenerative processes.

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.

Few-mode fibers (FMFs) 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 the retrieval of both the 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 to process intensity-only images, this drawback can be overcome. We introduce the mode transformer network, which can 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 using 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 for 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.