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Polarization-resolved second-harmonic generation imaging through a multimode fiber
Multimode fiber-based endoscopes have recently emerged as a tool for minimally invasive endoscopy in tissue, at depths well beyond the reach of multiphoton imaging. Here, we demonstrate label-free second-harmonic generation (SHG) microscopy through such a fiber endoscope. We simultaneously fully control the excitation polarization state and the spatial distribution of the light at the fiber tip, and we use this to implement polarization-resolved SHG imaging, which allows imaging and identification of structural proteins such as collagen and myosin. We image mouse tail tendon and heart tissue, employing the endoscope at depths up to 1 mm, demonstrating that we can differentiate these structural proteins. This method has the potential for enabling instant and in situ diagnosis of tumors and fibrotic conditions in sensitive tissue with minimal damage.
Hybrid Optical Fibers – An Innovative Platform for In‐Fiber Photonic Devices
The field of hybrid optical fibers is one of the most active research areas in current fiber optics and has the vision of integrating sophisticated materials inside fibers, which are not traditionally used in fiber optics. Novel in-fiber devices with unique properties have been developed, opening up new directions for fiber optics in fields of critical interest in modern research, such as biophotonics, environmental science, optoelectronics, metamaterials, remote sensing, medicine, or quantum optics. Here the recent progress in the field of hybrid optical fibers is reviewed from an application perspective, focusing on fiber-integrated devices enabled by including novel materials inside polymer and glass fibers. The topics discussed range from nanowire-based plasmonics and hyperlenses, to integrated semiconductor devices such as optoelectronic detectors, and intense light generation unlocked by highly nonlinear hybrid waveguides.
Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning
Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.