Clinical instrumentation and applications of Raman spectroscopy

Abstract

Clinical diagnostic devices provide new sources of information that give insight about the state of health which can then be used to manage patient care. These tools can be as simple as an otoscope to better visualize the ear canal or as complex as a wireless capsule endoscope to monitor the gastrointestinal tract. It is with tools such as these that medical practitioners can determine when a patient is healthy and to make an appropriate diagnosis when he/she is not. The goal of diagnostic medicine then is to efficiently determine the presence and cause of disease in order to provide the most appropriate intervention. The earliest form of medical diagnostics relied on the eye - direct visual observation of the interaction of light with the sample. This technique was espoused by Hippocrates in his 5th century BCE work Epidemics, in which the pallor of a patient's skin and the coloring of the bodily fluids could be indicative of health. In the last hundred years, medical diagnosis has moved from relying on visual inspection to relying on numerous technological tools that are based on various types of interaction of the sample with different types of energy - light, ultrasound, radio waves, X-rays etc. Modern advances in science and technology have depended on enhancing technologies for the detection of these interactions for improved visualization of human health. Optical methods have been focused on providing this information in the micron to millimeter scale while ultrasound, X-ray, and radio waves have been key in aiding in the millimeter to centimeter scale. While a few optical technologies have achieved the status of medical instruments, many remain in the research and development phase despite persistent efforts by many researchers in the translation of these methods for clinical care. Of these, Raman spectroscopy has been described as a sensitive method that can provide biochemical information about tissue state while maintaining the capability of delivering this information in real-time, non-invasively, and in an automated manner. This review presents the various instrumentation considerations relevant to the clinical implementation of Raman spectroscopy and reviews a subset of interesting applications that have successfully demonstrated the efficacy of this technique for clinical diagnostics and monitoring in large (n ≥ 50) in vivo human studies.

Fig. 1

Raman spectrum of phosphatidylcholine, a phospholipid known to be present in cells and tissues, measured using a fiber optic probe based Raman system at 785 nm excitation. Characteristic spectral peaks correspond to molecular vibrations of the molecule of interest.

Fig. 2

Basic schematic of an optical (including Raman) spectroscopic system.

Fig. 3

Raman scattering and autofluorescence polynomial fit signals for (A) breast and (B) kidney tissues measured ex vivo at 785 nm (blue) and 1064 nm (green) excitation wavelengths. Strong Raman features of breast tissue are apparent despite tissue background while low Raman intensities of the kidney are completely overwhelmed by the strong intrinsic signal at 785 nm but more readily visible at 1064 nm.

Fig. 4

Flowchart for typical system calibration and signal processing procedures for clinical Raman spectroscopy systems. The darker shaded boxes indicate steps that require collection of reference spectra prior to data acquisition while the other steps can be implemented in-line per spectrum for system automation.

Fig. 5

Diagram for a comparison study of clinical Raman spectroscopy system components. By varying combinations of instruments, variability studies have investigated the impact of unique components on the acquired spectral signatures. Results show that the collection leg of the system and the design of the fiber probe have the most significant contribution to the instrument variance observed in the spectra.

Fig. 6

Representative Raman spectral differences that can be obtained from a single sample acquired using two different probes. (A) A conventional filtered Raman probe (green) and a beam-steered Raman probe (blue) on skin in vivo demonstrate unique lineshapes. (B) Two iterations of the same probe design used to measure an albumin sample demonstrate the effect of slightly different inline filters on the raw and resulting processed (inset) Raman spectrum.

Fig. 7

Impact of background elimination varies based on the sample or tissue measured. Fluorescence subtraction using (A) 5th order polynomial in skin and cervix yield unique shapes due to compositional differences. (B) Using a 5th versus 7th order polynomial in colon (processed spectra inset) demonstrates that a single polynomial fitting order may not be appropriate for all samples; higher order polynomials are used to simulate background fluorescence and subtracted to enhance the underlying Raman signal from the sample.

Fig. 8

Fingerprint and high-wavenumber Raman spectrum of ex vivo breast tissue depicts the broad, strong features characteristic of lipid components in the tissue. Both segments of the Raman spectrum can provide valuable information for evaluation of complex sample composition.