Raf Van de Plas (Presenter)
Delft University of Technology
Bio: Raf Van de Plas is assistant professor at the Delft University of Technology (TU Delft) in the Netherlands. At the Delft Center for Systems and Control, he works on computational analysis of molecular imaging modalities such as imaging mass spectrometry and microscopy. He holds an adjunct assistant professor position in biochemistry at Vanderbilt University School of Medicine in Nashville, TN. He holds a PhD in Engineering (KU Leuven, 2010), an MSc in Industrial Engineering (Group T, 2002), and an MSc in Artificial Intelligence (KU Leuven, 2003). Before his appointment at TU Delft, he held a research faculty position at the Mass Spectrometry Research Center at Vanderbilt University with Dr. Richard Caprioli. His research focuses on the interface between (i) mathematical engineering and machine learning; (ii) analytical chemistry and instrumentation; and (iii) life sciences and medicine.
Authorship: Raf Van de Plas (1,2), Jeffrey Spraggins (2), Nico Verbeeck (1), Junhai Yang (2), and Richard M. Caprioli (2).
(1) Delft University of Technology, Delft, The Netherlands; (2) Vanderbilt University, Nashville, TN, USA.
| Short Abstract Data-driven multi-modal image fusion enables integration of imaging mass spectrometry (IMS) with other imaging technologies. Applications include the estimation of molecular distributions in tissue that is not physically measured by IMS, and prediction of ion distributions to a spatial resolution that exceeds that of measured ion images by ten times or more. The ability to predict an ion’s localization using another modality depends on whether that ion species has a detectable relationship to the other modality. We examine for different biomolecular classes, including lipids and proteins, the relationship between fusion performance and the modality type that IMS is integrated with. |
Long Abstract
Data-driven multi-modal image fusion enables integration of imaging mass spectrometry (IMS) with other imaging technologies. Applications include the estimation of molecular distributions in tissue that is not physically measured by IMS, and prediction of ion distributions to a spatial resolution that exceeds that of measured ion images by ten times or more. The ability to predict an ion’s localization using another modality depends on whether that ion species has a detectable relationship to the other modality. We examine for different biomolecular classes, including lipids and proteins, the relationship between fusion performance and the modality type that IMS is integrated with.
References & Acknowledgements:
Van de Plas R., Yang J., Spraggins J., and Caprioli R.M., Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping. Nature methods, 2015, 12(4), pp.366-372.
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