Stimulated Raman scattering (SRS) microscopy is a label-free quantitative chemical imaging technique that has demonstrated great utility in biomedical imaging applications ranging from real-time stain-free histopathology to live animal imaging. However, similar to many other nonlinear optical imaging techniques, SRS images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light in tissue as well as the limitation in applicable power to minimize photodamage. We present the use of a deep learning algorithm to significantly improve the SNR of SRS images. Our algorithm is based on a U-Net convolutional neural network (CNN) and significantly outperforms existing denoising algorithms. More importantly, we demonstrate that the trained denoising algorithm is applicable to images acquired at different zoom, imaging power, imaging depth, and imaging geometries that are not included in the training. Our results identify deep learning as a powerful denoising tool for biomedical imaging at large, with potential towards in vivo applications, where imaging parameters are often variable and ground-truth images are not available to create a fully supervised learning training set.

Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.

Stimulated Raman scattering (SRS) microscopy is a promising technique for studying tissue structure, physiology, and function. Similar to other nonlinear optical imaging techniques, SRS is severely limited in imaging depth due to the turbidity and heterogeneity of tissue, regardless of whether imaging in the transmissive or epi mode. While this challenge is well known, important imaging parameters (namely maximum imaging depth and imaging signal to noise ratio) have rarely been reported in the literature. It is also important to compare epi mode and transmissive mode imaging to determine the best geometry for many tissue imaging applications. In this manuscript we report the achievable signal sizes and imaging depths using a simultaneous epi/transmissive imaging approach in four different murine tissues; brain, lung, kidney, and liver. For all four cases we report maximum signal sizes, scattering lengths, and achievable imaging depths as a function of tissue type and sample thickness. We report that for murine brain samples thinner than 2 mm transmissive imaging provides better results, while samples 2 mm and thicker are best imaged with epi imaging. We also demonstrate the use of a CNN-based denoising algorithm to yield a 40 µm (24%) increase in achievable imaging depth.

Hyperspectral stimulated Raman scattering (hsSRS) microscopy has recently emerged as a powerful non-destructive technique for the label-free chemical imaging of biological samples. In most hsSRS imaging experiments, the SRS spectral range is limited by the total bandwidth of the excitation laser to ~300 cm −1 and a spectral resolution of ~25 cm −1 . Here we present a novel approach for broadband hsSRS microscopy based on parabolic fiber amplification to provide linearly chirped broadened Stokes pulses. This novel hsSRS instrument provides >600 cm −1 spectral coverage and ~10 cm −1 spectral resolution. We further demonstrated broadband hsSRS imaging of the entire Raman fingerprint region for resolving the distribution of major biomolecules in fixed cells. Moreover, we applied broadband hsSRS in imaging amyloid plaques in human brain tissue with Alzheimer's disease.