Details

Autor(en) / Beteiligte

• Camp Jr, Charles H.
• Lee, Young Jong
• Cicerone, Marcus T.

Titel

Quantitative, comparable coherent anti-Stokes Raman scattering (CARS) spectroscopy: correcting errors in phase retrieval

Ist Teil von

• Journal of Raman spectroscopy, 2016-04, Vol.47 (4), p.408-415

Ort / Verlag

England: Blackwell Publishing Ltd

Erscheinungsjahr

2016

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• Coherent anti‐Stokes Raman scattering (CARS) microspectroscopy has demonstrated significant potential for biological and materials imaging. To date, however, the primary mechanism of disseminating CARS spectroscopic information is through pseudocolor imagery, which explicitly neglects a vast majority of the hyperspectral data. Furthermore, current paradigms in CARS spectral processing do not lend themselves to quantitative sample‐to‐sample comparability. The primary limitation stems from the need to accurately measure the so‐called nonresonant background (NRB) that is used to extract the chemically sensitive Raman information from the raw spectra. Measurement of the NRB on a pixel‐by‐pixel basis is a nontrivial task; thus, surrogate NRB from glass or water is typically utilized, resulting in error between the actual and estimated amplitude and phase. In this paper, we present a new methodology for extracting the Raman spectral features that significantly suppresses these errors through phase detrending and scaling. Classic methods of error correction, such as baseline detrending, are demonstrated to be inaccurate and to simply mask the underlying errors. The theoretical justification is presented by re‐developing the theory of phase retrieval via the Kramers–Kronig relation, and we demonstrate that these results are also applicable to maximum entropy method‐based phase retrieval. This new error‐correction approach is experimentally applied to glycerol spectra and tissue images, demonstrating marked consistency between spectra obtained using different NRB estimates and between spectra obtained on different instruments. Additionally, in order to facilitate implementation of these approaches, we have made many of the tools described herein available free for download. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Quantitatively reliable hyperspectral CARS imagery is significantly hampered by experimental and processing conditions that induce phase and amplitude errors, limiting intra‐ and inter‐sample comparisons, and those from different platforms. We demonstrate the provenance of these errors and an in silico procedure for robust Raman signature extraction. These quantitative, comparable spectra enable extraction of biological conclusions not just from the pseudocolor imagery, but from the dense spectral content, and facilitate dissemination of hyperspectral cubes for community data mining.