Fusing ToF-SIMS images for spatial-spectral resolution enhancement using a convolutional neural network

Gardner, Wil; Winkler, David; Maliki, Ruqaya; Cutts, Suzanne; Ellis, Sarah; Anderson, Robin; Muir, Benjamin; Pigram, Paul

doi:10.26181/22493398.v2

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Fusing ToF-SIMS images for spatial-spectral resolution enhancement using a convolutional neural network

Version 2 2024-07-11, 06:17

Version 1 2023-04-03, 04:11

journal contribution

posted on 2024-07-11, 06:17 authored by Wil GardnerWil Gardner, David WinklerDavid Winkler, Ruqaya MalikiRuqaya Maliki, Suzanne CuttsSuzanne Cutts, Sarah EllisSarah Ellis, Robin AndersonRobin Anderson, Benjamin MuirBenjamin Muir, Paul PigramPaul Pigram

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.

Funding

The authors gratefully acknowledge Borodinov et al.[21] for providing their code and for their correspondences in relation to their implementation of registration and data fusion methods. The authors also gratefully acknowledge that this research was supported by a grant from the Australian National Breast Cancer Foundation. This work was also supported by an Office of National Intelligence - National Intelligence and Security Discovery Research Grant (NI210100127) funded by the Australian Government. This work was performed in part at the Australian National Fabrication Facility (ANFF), a company established under the National Collaborative Research Infrastructure Strategy, through the La Trobe University Centre for Materials and Surface Science. The use of animals and drug treatment protocols were approved by the Animal Ethics Committee at La Trobe University (Melbourne, VIC, Australia) under ethics number AEC15-88. All animal experiments complied with all provisions of the Prevention of Cruelty to Animals Act, 1986, and the Australian Code for the Care and Use of Animals for Scientific Purposes, eighth edition, 2013. Open access publishing facilitated by La Trobe University, as part of the Wiley - La Trobe University agreement via the Council of Australian University Librarians.

History

Publication Date

2022-09-30

Journal

Advanced Materials Interfaces

Volume

9.

Issue

34

Article Number

2201464

Pagination

17p.

Publisher

Wiley

ISSN

2196-7350

Rights Statement

© 2022 The Authors. Advanced Materials Interfaces published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.