Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Abstract

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.

Keywords: Raman spectroscopy; biomedical; label-free; multimodal; optical coherence tomography.

FIGURE 1

OCT signal processing workflow, modified from reference [60]

FIGURE 2

Unique light source requirements for RS and OCT in terms of laser wavelength and bandwidth. Dotted lines represent the spectral regions for Raman emissions

FIGURE 3

Schematic of a multimodal RS and SD-OCT system with separate detection sub-systems, combined through a shared sample arm. This example shows spectral channels separated by dichroic filters (DF), and detected by dedicated spectrometers with collimating lens (CL), diffraction grating (DG), and charge-coupled device (CCD)

FIGURE 4

Multimodal RS-OCT system implemented using a shared sample arm and common detector. BP, band pass filter; DM, dichroic mirror; LP, long pass filter; MOS, MEMS optical switch; ND, neutral density filter; PC, polarization control paddles; SF, spatial filter; TM, translatable mirror; WC, water filled cuvette; XY, scanning galvanometer pair. Figure reproduced from [73] with permission

FIGURE 5

Miniature RS–OCT probes (A) handheld probe for real-time tissue measurements developed by Wang et al. [78]. (B) 3D technical model of the optomechanical probe developed by Klemes et al. [79] (C) 3D CAD design of the RS-OCT probe developed by Mazurenka et al. [80]. Figure reproduced from references [–80] with permissions

FIGURE 6

Coregistered RS-OCT morphomolecular maps of a bladder biopsy. Raman component coefficient maps of epithelium (A), collagen (B), and lipid (C) show their presence and distribution. (D) An en-face maximum intensity projection of the OCT volume between 150 and 180 μm. (e, f) Maximum intensity projections of 10 cross-sectional B-scans, positions indicated by dashed and dotted lines in the Enface OCT image with corresponding Raman component values (E: Epithelium, red, C: Collagen, green, L: Lipid, blue). An overlap of lipid Raman signals can be seen in black voids within OCT images. (g) H&E image of the biopsy. Arrows indicate lipid pools (blue arrow), epithelium (red arrow), lamina propria (green arrow), and collagen (green arrow). Scale bars: 250 μm. Figure reproduced from reference [87] with permission

FIGURE 7

Multimodal RS-OCT application domain. (A) In vivo versus ex vivo, (B) cancer versus other applications, and (C) scope of various tissue types explored with RS–OCT.

FIGURE 8

RS–OCT data from ex vivo breast tissues. Structural anomalies within the OCT image are spectrally characterized by RS, showing pronounced protein peaks indicative of malignancy (solid curve), while normal breast tissue exhibited a strong lipid signature (dotted curve). Scale bar represents 500 μm. Reproduced with permissions from reference [76]