Issue 24, 2023

Acceleration of high-quality Raman imaging via a locality enhanced transformer network

Abstract

Raman imaging (RI) is an outstanding technique that enables molecular-level medical diagnostics and therapy assessment by providing characteristic fingerprint and morphological information about molecules. However, obtaining high-quality Raman images generally requires a long acquisition time, up to hours, which is prohibitive for RI applications of timely cytopathology and histopathology analyses. To address this issue, image super-resolution (SR) based on deep learning, including convolutional neural networks and transformers, has been widely recognized as an effective solution to reduce the time required for achieving high-quality RI. In this study, a locality enhanced transformer network (LETNet) is proposed to perform Raman image SR. Specifically, the general architecture of the transformer is adopted with the replacement of self-attention by convolution to generate high-fidelity and detailed SR images. Additionally, the convolution in the LETNet is further optimized by utilizing depth-wise convolution to improve the computational efficiency of the model. Experiments on hyperspectral Raman images of breast cancer cells and Raman images of a few channels of brain tumor tissues demonstrate that the LETNet achieves superior 2×, 4×, and 8× SR with fewer parameters compared with other SR methods. Consequently, high-quality Raman images can be obtained with a significant reduction in time, ranging from 4 to 64 times. Overall, the proposed method provides a novel, efficient, and reliable solution to expedite high-quality RI and promote its application in real-time diagnosis and therapy.

Graphical abstract: Acceleration of high-quality Raman imaging via a locality enhanced transformer network

Supplementary files

Article information

Article type
Paper
Submitted
08 sep 2023
Accepted
27 okt 2023
First published
04 nov 2023

Analyst, 2023,148, 6282-6291

Acceleration of high-quality Raman imaging via a locality enhanced transformer network

S. Weng, R. Zhu, Y. Wu, C. Wang, P. Li, L. Zheng, D. Liang and Z. Duan, Analyst, 2023, 148, 6282 DOI: 10.1039/D3AN01543B

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