Shafe-Purcell, John

Coherent anti-Stokes Raman scattering (CARS) microscopy is a label-free chemical imaging modality that uses CARS as a contrast mechanism to spatially resolve materials based on their molecular vibrational spectra. Due to the presence of a non resonant background that obfuscates the chemical information contained in CARS spectra, CARS images suffer from poor contrast and cannot be readily used for quantitative chemical analysis. Over the past two decades, spectral retrieval methods have been developed to obtain Raman-like spectra from CARS spectra. These methods promise to improve image contrast and enable reliable quantitative analysis. In this work I systematically evaluate a selection of the forefront spectral retrieval methods, including both analytical and machine learning approaches, to determine how well they perform at the task of non resonant background removal. The more recent machine learning methods demonstrate remarkable performance on spectra resembling the training dataset but are not as suitable as the analytical methods in general. The analytical methods based on the discrete Hilbert transform thus remain preferable due to their ease-of-use and general applicability.

Author Keywords: chemical imaging, coherent anti-stokes raman scattering, kramers-kronig analysis, machine learning, non-resonant background, spectral phase retrieval