| AVS 63rd International Symposium & Exhibition | |
| Applied Surface Science | Tuesday Sessions |
| Session AS+SS-TuA |
| Session: | Data Analytics in Surface Science and Nanoscience |
| Presenter: | Bonnie June Tyler, Universität Münster, Germany |
| Authors: | B.J. Tyler, Universität Münster, Germany H.F. Arlinghaus, University of Muenster, Germany |
| Correspondent: | Click to Email |
As mass spectrometry imaging (MSI) has moved from the technique development stage into real world biological studies, the need to combine mass spectrometry images with other biologically relevant imaging techniques has become important. Techniques as diverse as electron microscopy, scanning probe microscopy, XPS imaging, H&E staining, and fluorescent labeling can provide important information that is complementary to the mass spectral images. Combining the information from these complementary measurements is often necessary for accurate understanding of biological samples. Within the field of mass spectrometry imaging alone, combining different imaging modes, such as MALDI/ToF-SIMS, or GCIB ToF-SIMS/LMIG ToF-SIMS, can enhance understanding of the specimens being studied.
In theory, more data should enable more confident conclusions. In practice, however, the challenges of handling and reducing very large imaging data sets, that have disparities in spatial resolution and contrast mechanisms, can result in biased or misleading conclusions. In order to facilitate more consistent, accurate and useful descriptions of real world samples, advanced data exploration tools are needed. Image fusion is an approach to combining data from different sources that is receiving increasing attention within the field of mass spectrometry imaging.
Although many algorithms for image fusion have been developed for applications in remote sensing, medical imaging and photography, the distinctive features of mass spectrometry make many of these techniques inappropriate for use in this field. We have tested algorithms from two major classes of image fusion, those that operate in the spatial domain and those that operate in the frequency domain. Common artefacts caused by the different algorithms have been identified. Two modified algorithms have been developed which can be used to produce satisfactory fused images using mass spectrometry data. The first approach combines multivariate analysis (MVA) and discreet cosine transform (DCT) and is useful for combining MSI images with monochromatic images. The second algorithm, which uses a combination of multivariate methods, is useful for fusing MSI data with a second spectral image. Both of these new image fusion approaches have been tested on simulations, model systems and real tissue samples. We have shown that MVA image fusion can be a valuable technique for reducing noise, improving image contrast and enhancing the sharpness of mass spectrometry images. With appropriate attention to the distinctive features of each imaging method, image fusion can be done without significant artefacts or distortion of the spectral detail.