  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, reference NRB from
glass or water are typically utilized, resulting in error between the actual
and estimated amplitude and phase. In this manuscript, 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.
