Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepF...

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Published in:Analytical chemistry (Washington) 2023-06, Vol.95 (25), p.9714-9721
Main Authors: Guo, Lei, Zhu, Jinyu, Wang, Keqi, Cheng, Kian-Kai, Xu, Jingjing, Dong, Liheng, Xu, Xiangnan, Chen, Can, Shah, Mudassir, Peng, Zhangxiao, Wang, Jianing, Cai, Zongwei, Dong, Jiyang
Format: Article
Language:English
Subjects:
analytical chemistry
Chemistry
Computer vision
Deep learning
eosin
High resolution
Ill posed problems
image analysis
Image enhancement
Image processing
Image Processing, Computer-Assisted - methods
Image reconstruction
Image registration
Image resolution
Inspection
Mass spectrometry
Mass Spectrometry - methods
Mass spectroscopy
Microscopy
Optimization
quantitative analysis
Scientific imaging
Spatial data
Spatial discrimination
Spatial resolution
Transcriptomics
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Summary:High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.
ISSN:0003-2700
1520-6882
1520-6882
DOI:10.1021/acs.analchem.3c02002