High-throughput molecular imaging via deep learning enabled Raman spectroscopy

TL;DR

DeepeR leverages deep learning to significantly accelerate Raman spectroscopy, enabling high-resolution, high-throughput molecular imaging in biomedical applications by denoising, super-resolution, and transfer learning.

Contribution

The paper introduces DeepeR, a comprehensive deep learning framework that enhances Raman imaging speed and quality, surpassing previous methods and extending to tissue-scale imaging.

Findings

Achieved up to 160x speed-up in Raman imaging.

Improved denoising accuracy with 9x error reduction.

Extended imaging from cells to tissues using transfer learning.

Abstract

Raman spectroscopy enables non-destructive, label-free imaging with unprecedented molecular contrast but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep learning enabled Raman spectroscopy, termed DeepeR, trained on a large dataset of hyperspectral Raman images, with over 1.5 million spectra (400 hours of acquisition) in total. We firstly perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 9x improvement in mean squared error over state-of-the-art Raman filtering methods. Next, we develop a neural network for robust 2-4x super-resolution of hyperspectral Raman images that preserves molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 160x, enabling high resolution, high signal-to-noise ratio cellular imaging in under one minute. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.