Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue

TL;DR

This paper presents a machine learning approach for real-time, accurate inference of molecular composition in brain tissue using diffuse spectroscopy, enhancing surgical monitoring with non-invasive optical methods.

Contribution

It introduces modifications to existing Beer-Lambert law-based methods, enabling fast and accurate real-time molecular composition inference from optical spectra.

Findings

Method achieves real-time inference with high accuracy.

Spectral unmixing reveals brain anatomy features.

Applicable to linear and non-linear spectral models.

Abstract

Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) 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, no established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. In this paper, we analyse a machine learning technique for fast and accurate inference of changes in the molecular composition of brain tissue. We reconsider and propose modifications to the existing learnable methodology based on the Beer-Lambert law, which analytically connects the spectra with concentrations. We evaluate the method's applicability to linear and non-linear formulations of the Beer-Lambert law. The approach is tested on real data obtained from the bNIRS- and HSI-based optical monitoring of brain tissue. 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. Preliminary findings show that Beer-Lambert law-based spectral unmixing allows to contrast brain anatomy semantics such as the vessel tree and tumor area.