Reconstruction and feature selection for desorption electrospray ionization mass spectroscopy imagery

Yi Gao; Liangjia Zhu; Isaiah Norton; Nathalie Y. R. Agar; Allen Tannenbaum

doi:10.1117/12.2043273

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12 March 2014 Reconstruction and feature selection for desorption electrospray ionization mass spectroscopy imagery

Yi Gao, Liangjia Zhu, Isaiah Norton, Nathalie Y. R. Agar, Allen Tannenbaum

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Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 90360D (2014) https://doi.org/10.1117/12.2043273
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 10⁴ to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for further pathological analysis. The methods are validated on brain and breast tumor data sets.

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Yi Gao, Liangjia Zhu, Isaiah Norton, Nathalie Y. R. Agar, and Allen Tannenbaum "Reconstruction and feature selection for desorption electrospray ionization mass spectroscopy imagery", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90360D (12 March 2014); https://doi.org/10.1117/12.2043273

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