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Proceedings Paper

Reconstruction and feature selection for desorption electrospray ionization mass spectroscopy imagery
Author(s): Yi Gao; Liangjia Zhu; Isaiah Norton; Nathalie Y. R. Agar; Allen Tannenbaum
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Paper 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.

Paper Details

Date Published: 12 March 2014
PDF: 6 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90360D (12 March 2014); doi: 10.1117/12.2043273
Show Author Affiliations
Yi Gao, The Univ. of Alabama at Birmingham (United States)
Liangjia Zhu, Stony Brook Univ. (United States)
Isaiah Norton, Brigham And Women's Hospital (United States)
Nathalie Y. R. Agar, Brigham and Women's Hospital (United States)
Harvard Medical School (United States)
Allen Tannenbaum, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes III, Editor(s)

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