Mass image quality improvement using a machine learning model "FINE-AI Filter"
MSTips No. 526
Mass imaging (MSI) using matrix-assisted laser desorption/ionization (MALDI) have seen increasing use in various fields in recent years. The soft ionization method of MALDI makes it possible to visualize the localization of various molecules. However, MALDI-MSI suffers from a low signal-to-noise ratio in extracted mass images, mainly for the following two reasons: I) Non-uniformity occurs in the crystal morphology of the matrix sprayed onto the sample surface. II) Each pixel is a local analysis of approximately several tens of micrometers square, resulting in a small number of ions obtained from that region. These issues are particularly pronounced in extracted mass images of trace components with low peak intensity. Therefore, in this report, we attempt to improve the image quality of extracted mass images using the FINE-AI Filter, which was developed by applying a machine-learning model for noise filtering—originally created from secondary electron images obtained by scanning electron microscopy (SEM)—to MALDI-MSI.