High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Raman spectroscopy enables nondestructive, 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 data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10x improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4X spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90X, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160x, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. 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.

Automated summary

By utilizing deep learning, denoising and reconstructing low signal-to-noise ratio Raman molecular signatures is achieved, with a 10x improvement in mean-squared error over common Raman filtering methods. A neural network for robust 2-4X spatial super-resolution of hyperspectral Raman images is developed, resulting in Raman imaging speed-ups of up to 40-90X, enabling good-quality cellular imaging in under 1 min. Transfer learning is then applied to extend DeepeR from cell to tissue-scale imaging, providing a foundation for higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.