Reliable and Accurate Registration and Review of Elemental and Molecular Imaging Data

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This project is directly aligned with NPL strategic priorities in personalised medicine, early diagnosis, multi-modal imaging, complex systems research. The project is directly aligned to EPSRC priorities 'imaging modalities, novel analytical techniques and innovation needed to improve prediction, diagnosis and treatment of disease'. Combining and analysing data from multiple modalities is vital to fully understand complex biological processes that occur on different length and molecular scales. For examples the analysis of correlation between metabolites from mass spectrometry and information from histochemistry and immunohistochemistry can relate metabolic activity to structural and functional information in diseased tissues. Disparity in spatial resolution can make analysis of correlations and overlap difficult as a small well resolved feature in one modality may appear large and blurred in another. Additionally, is it challenging to separate absolute measures of correlation between variables from accuracy in the registration process. We will seek to acquire matched datasets of elemental and molecular information using particle-induced X-ray emission (PIXE) and mass spectrometry imaging (MSI) respectively, as well as histological information from haematoxylin and eosin (H&E). After registration, we will develop methods to correct for image blur between two mismatched spatial resolutions, and methods and metrics to assess the quality of the registration such as structural similarity index.NPL is currently leading a major consortium, funded by CRUK to establish multimodal mass spectrometry imaging pipelines to deliver spatially resolved integrated maps of tumour metabolism over wide ranging length scales. Multimodal approaches are also being established at The University of Surrey, with a different focus of combining molecular (typically metabolic) features with elemental mapping methods (EPSRC Fellowship awarded to Prof. M. Bailey). New data integration routines are identified as areas of ongoing need in both programmes. It is timely to establish new collaborative research to address this.