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Compressive Raman microspectroscopy (Conference Presentation)
Author(s): Hilton B. de Aguiar
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Paper Abstract

Raman imaging is recognized as a powerful label-free approach to provide contrasts based on chemical selectivity. Nevertheless, Raman-based microspectroscopy still have drawbacks. The first issue is the inherent high data throughput in Raman microspectroscopy, posing challenges for dynamic and large-scale imaging, and its subsequent data storage. The second issue is data presentation: often, Raman microspectroscopy acquires overwhelming data sets, which information is then post-processed to a more useful and straightforward presentation (typically limited to the number of different target chemicals in a system). In parallel, compressive sensing has shown a paradigm shift approach where one can obtain accurate information from fewer samples than assumed by Nyquist-Shannon sampling theorem. A key concept in compressive sensing is to recognize that data sparsity can be exploited to reconstruct data that has been considerably undersampled. Following the compressive sensing spirit, various approaches were proposed to demonstrate compressive Raman microspectroscopy in different flavors, exploiting the fact that both the vibrational spectrum and the chemical components are often sparse. In this contribution, I will discuss different ways of performing compressive Raman, in particular focusing on the challenges that precludes fast imaging of biological specimens, and how we recently tackled them. With these outcomes, compressive Raman imaging soon may be routinely used by non-specialists in vibrational spectroscopy in a “blind” manner.

Paper Details

Date Published: 4 March 2019
Proc. SPIE 10882, Multiphoton Microscopy in the Biomedical Sciences XIX, 1088223 (4 March 2019); doi: 10.1117/12.2510566
Show Author Affiliations
Hilton B. de Aguiar, École normale supérieure Paris-Saclay (France)

Published in SPIE Proceedings Vol. 10882:
Multiphoton Microscopy in the Biomedical Sciences XIX
Ammasi Periasamy; Peter T. C. So; Karsten König, Editor(s)

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