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Time-resolved diffuse optical tomography system based on adaptive structured light illumination and compressive sensing detection

Farina, A; Betcke, M; Bassi, A; Valentini, G; Arridge, S; D'Andrea, C; (2018) Time-resolved diffuse optical tomography system based on adaptive structured light illumination and compressive sensing detection. In: Fournier, C and Georges, MP and Popescu, G, (eds.) Unconventional Optical Imaging. (pp. 106770D). Society of Photo-Optical Instrumentation Engineers (SPIE) Green open access

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Abstract

Diffuse Optical Tomography (DOT)is a powerful tool for the reconstruction of optical properties inside a diffusive medium, such as biological tissues. In particular, in the last years, techniques based on structured light illumination and compressive sensing detection have been developed. In this work a time-resolved system based on structured light illumination and compressive detection has been developed and used for DOT. Moreover, a data-driven algorithm for optimal pattern generation based on the Singular-Value Decomposition has been implemented and validated.

Type: Proceedings paper
Title: Time-resolved diffuse optical tomography system based on adaptive structured light illumination and compressive sensing detection
Event: SPIE Photonics Europe, 22-26 April 2018, Strasbourg, France
ISBN-13: 9781510618800
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2306697
Publisher version: https://doi.org/10.1117/12.2306697
Language: English
Additional information: This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Diffuse Optical Tomography, structured light, adaptive algorithm, time-resolved, compressive sensing
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10055952
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