Research on super-resolution reconstruction of mass spectrometry imaging using spatially multi-level and self-supervised deep learning network
10.19745/j.1003-8868.2025059
- VernacularTitle:基于空间多层次自监督深度学习网络的质谱成像超分辨重建方法研究
- Author:
Chao-long LIN
1
;
Hui YANG
;
Ya-hui GE
Author Information
1. 北京大学医学部医学技术研究院,北京 100083
- Publication Type:Journal Article
- Keywords:
mass spectrometry imaging;
super-resolution reconstruction;
deep learning;
multimodal image fusion;
self-supervised learning
- From:
Chinese Medical Equipment Journal
2025;46(4):1-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a method for mass spectrometry imaging(MSI)super-resolution reconstruction based on a spatially multi-level and self-supervised deep learning network(SMSDL-Net),aiming to improve the resolution of mass spectrometry images.Methods SMSDL-Net firstly registered histological and mass spectrometry images using a nonlinear transformation.Then a multi-branch Vision Transformer(ViT)was utilized to extract hierarchical features of high-resolution histological images in a self-supervised manner.These features were subsequently combined with the paired low-resolution mass spectrometry data to construct a regression network,which could realize the prediction of high-resolution mass spectrometry information.To validate the performance of the proposed method,the results by the method were compared with those of the traditional bicubic interpolation(BI)methods based on interpolation processing and the deepFERE method based on convolution neural network(CNN)for super-resolution reconstruction of magnesium elemental image of metal mass spectrometry of human liver cancer samples,and the method was also applied to a mouse renal adenocarcinoma metabolite mass spectrometry imaging dataset.Results Compared with the traditional BI methods and the deepFERE multimodal method,the method proposed demonstrated the lowest root mean square error(RMSE=0.015),the highest structural similarity index measure(SSIM=0.84)and the highest R-squared value(R2=0.853)in reconstructing mass spectrometry images.The effectiveness of the method and its potential for precise tissue-specific distinction were validated using the mouse renal adenocarcinoma metabolite MSI dataset.Conclusion Compared with traditional single-modal and pixel-wise regression deep learning methods,the method proposed enhances the quality of high-resolution mass spectrometry image reconstruction and can serve as a novel method for super-resolution reconstruction in the field of mass spectrometry imaging.[Chinese Medical Equipment Journal,2025,46(4):1-8]
