Fusing ToF‐SIMS Images for Spatial‐Spectral Resolution Enhancement using a Convolutional Neural Network
Wil Gardner, David A. Winkler, Ruqaya Maliki, Suzanne M. Cutts, Sarah Ellis, Robin L. Anderson, Benjamin W. Muir, Paul J. Pigram
Abstract
Abstract Hyperspectral data sets generated by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) contain valuable spatial‐spectral information characterizing the distribution of atomic and molecular species across a sample surface. Modern ToF‐SIMS instruments have high spatial resolution (in the order of tens of nanometers) relative to most other mass spectrometry imaging (MSI) techniques. However, there is generally a trade‐off between spatial and mass resolution when using different instrument modes. In this study, a convolutional neural network (CNN) fusion method is used to fuse correlated high spatial and high mass resolution ToF‐SIMS hyperspectral data sets. This process generates resolution‐enhanced data, which exhibit both high spatial and mass resolution. The CNN fusion method is applied to ToF‐SIMS images of a simple, well‐characterized gold mesh sample and a significantly more complex biological (tumor) tissue section. The method is compared to another linear fusion method used in the broader MSI community and a substantial improvement is found. This comparison focuses on both visual quality observations as well as statistical similarity measures. This work demonstrates the utility of the CNN fusion method for ToF‐SIMS data, enabling investigation of the atomic and molecular characteristics of surfaces at high spatial and mass spectral resolution.