MOSR (MSI from Optical Super-Resolution)

The article on Nature Machine Intelligence: A super-resolution strategy for mass spectrometry imaging via transfer learning

Liao, T., Ren, Z., Chai, Z. et al. A super-resolution strategy for mass spectrometry imaging via transfer learning. Nat Mach Intell 5, 656–668 (2023). https://doi.org/10.1038/s42256-023-00677-7

MOSR is a Mass Spectrometry Imaging (MSI) super-resolution strategy based on transfer learning, which tremendously reduces the requirement for sample size by transferring the knowledge learned from abundant optical images (~15,000 images) to the MSI model. Once trained, MOSR can take an Low-Resolution MSI image as input and output a reconstructed High-Resolution MSI image in less than one second.

Introduction

Mass spectrometry imaging (MSI) is a valuable tool for the spatial distribution analysis of chemicals in biological tissue. It has been proven to be extremely useful for applications such as the spatial distribution analysis of molecules in biological tissue, the identification of biomarkers in pathological tissue, and the evaluation of drug pharmacokinetic characteristics. The spatial resolution of MSI is a critical parameter that affects the precise assessment of chemical localizations. Deep learning (DL) utilizing plentiful images is often used to further improve resolution. However, due to limitations in the amount of High-Resolution MSI data publicly available, super-resolution reconstruction of MSI images based on DL is still a challenge.

System Requirements

Hardware requirements

We provide a DEMO model to show the complete pipeline. This DEMO requires only a standard computer with enough GPU memory (at least 5 GB, e.g. NVIDIA RTX3090) to load the model.

Software requirements

In this work, we used open source image and video restoration toolbox BasicSR to simply build ESRGAN models. Therefore, the software requirements are consistent with the toolbox BasicSR.

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7
• NVIDIA GPU + CUDA
• Linux

Installation Guide

• You can install the toolbox from PyPI, and it will take several minutes.
  

pip install basicsr

Demo

1. The pre-trained model is released as v1.0.0, and the DEMO Low-Resolution images are in the images/test folder.
2. Modify the config file in options/test/test_ESRGAN_x4_MOSR.yml and assign correct paths for the image and model.
3. Inference. Run the following command, and the images are reconstructed in less than one minute. The test.py is in the directory of BasicSR.
   

python basicsr/test.py -opt options/test/test_ESRGAN_x4_MOSR.yml