High-throughput molecular imaging via deep-learning-enabled Raman spectroscopy.
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, 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 160×, 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.
Date Issued
2021-12-07
Date Acceptance
2021-10-08
Citation
Analytical Chemistry, 2021, 93 (48), pp.15850-15860
ISSN
0003-2700
Publisher
American Chemical Society
Start Page
15850
End Page
15860
Journal / Book Title
Analytical Chemistry
Volume
93
Issue
48
Copyright Statement
© 2021 The Authors. Published by American Chemical Society. This work is published under CC BY-NC-ND licence.
Sponsor
Commission of the European Communities
Wellcome Trust
GlaxoSmithKline Services Unlimited
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/34797972
Grant Number
676137
098411/Z/12/Z
Mark Buswell
Subjects
Deep Learning
Molecular Imaging
Neural Networks, Computer
Signal-To-Noise Ratio
Spectrum Analysis, Raman
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2021-11-19