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Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery

Institution:
1Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
2Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, Alabama, USA.
3Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
Publication Date:
Mar-2014
Journal:
Proc Soc Photo Opt Instrum Eng
Volume Number:
9036
Pages:
90360D
Citation:
Proc Soc Photo Opt Instrum Eng. 2014 Mar 12;9036:90360D.
PubMed ID:
25302009
PMCID:
PMC4187386
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gao Y., Zhu L., Norton I., Agar N.Y.R., Tannenbaum A. Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery. Proc Soc Photo Opt Instrum Eng. 2014 Mar 12;9036:90360D. PMID: 25302009. PMCID: PMC4187386.
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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 pathological analysis. The methods are validated on brain and breast tumor data sets.

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