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Optical imaging modalities such as near-infrared spectroscopy (NIRS) and
hyperspectral imaging (HSI) represent a promising alternative for low-cost,
non-invasive, and fast monitoring of functional and structural properties of
living tissue. Particularly, the possibility of extracting the molecular
composition of the tissue from the optical spectra in real-time deems the
spectroscopy techniques as a unique diagnostic tool. However, due to a lack of
paired optical and molecular profiling studies, building a mapping between a
spectral signature and a corresponding set of molecular concentrations is still
an unsolved problem. Furthermore, there are currently no established methods to
streamline inference of the biochemical composition from the optical spectrum
for real-time applications such as surgical monitoring. In this paper, we
develop a technique for fast inference of changes in the molecular composition
of brain tissue. We base our method on the Beer-Lambert law to analytically
connect the spectra with concentrations and use a deep-learning approach to
significantly speed up the concentration inference compared to traditional
optimization methods. We test our approach on real data obtained from the
broadband NIRS study of piglets' brains and the HSI dataset of glioma patients.
The results demonstrate that the proposed method enables real-time molecular
composition inference while maintaining the accuracy of traditional linear and
non-linear optimization solvers.