Unsupervised data driven approaches to Raman imaging through a multimode optical fiber

TL;DR

This paper demonstrates a novel, miniaturized Raman imaging method using wavefront shaping through a single multimode fiber, combined with advanced statistical analysis for high-resolution chemical imaging.

Contribution

It introduces a wavefront shaping technique for Raman imaging through a single multimode fiber, enabling miniaturized, high-resolution chemical imaging without complex fiber arrays.

Findings

Achieved sub-cellular resolution Raman imaging through a single fiber.

Successfully applied PCA, t-SNE, UMAP, and k-means for data analysis.

Generated high-quality images of pharmaceutical microclusters.

Abstract

Raman spectroscopy is a label-free, chemically specific optical technique which provides detailed information about the chemical composition and structure of the excited analyte. Because of this, there is growing research interest in miniaturizing Raman probes to reach deep regions of the body. Typically, such probes utilize multiple optical fibers to act as separate excitation/collection channels with optical filters attached to the distal facet to separate the collected signal from the background optical signal from the probe itself. Although these probes have achieved impressive diagnostic performance, their use is limited by the overall size of the probe, which is typically several hundred micrometers to millimeters. Here, we show how a wavefront shaping technique can be used to measure Raman images through a single, hair thin multimode fiber. The wavefront shaping technique transforms the tip of the fiber to a sub-cellular spatial resolution Raman microscope. The resultant Raman images were analyzed with a variety of state-of-the-art statistical techniques including PCA, t-SNE, UMAP and k-means clustering. Our data-driven approach enables us to create high quality Raman images of microclusters of pharmaceuticals through a standard silica multimode optical fiber.