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dc.contributor.authorSoldevila, Fernando
dc.contributor.authorLenz, Armin
dc.contributor.authorGhezzi, Alberto
dc.contributor.authorFarina, Andrea
dc.contributor.authorD'Andrea, Cosimo
dc.contributor.authorTajahuerce, Enrique
dc.date.accessioned2021-10-14T12:13:22Z
dc.date.available2021-10-14T12:13:22Z
dc.date.issued2021-08-27
dc.identifier.citationF. Soldevila, A. J. M. Lenz, A. Ghezzi, A. Farina, C. D’Andrea, and E. Tajahuerce, "Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion," Opt. Lett. 46, 4312-4315 (2021)ca_CA
dc.identifier.issn0146-9592
dc.identifier.issn1539-4794
dc.identifier.urihttp://hdl.handle.net/10234/195010
dc.description.abstractTime-resolved fluorescence imaging is a key tool in biomedical applications, as it allows to non-invasively obtain functional and structural information. However, the big amount of collected data introduces challenges in both acquisition speed and processing needs. Here, we introduce a novel technique that allows to acquire a giga-voxel 4D hypercube in a fast manner while measuring only 0.03% of the dataset. The system combines two single-pixel cameras and a conventional 2D array detector working in parallel. Data fusion techniques are introduced to combine the individual 2D and 3D projections acquired by each sensor in the final high-resolution 4D hypercube, which can be used to identify different fluorophore species by their spectral and temporal signatures.ca_CA
dc.format.extent5 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherOptical Society of Americaca_CA
dc.relation.isPartOfOptics Letters Vol. 46, Issue 17, (2021)ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectfluorescenceca_CA
dc.titleGiga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusionca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1364/OL.434127
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/acceptedVersionca_CA
project.funder.nameMinisterio de Ciencia, Innovación y Universidadesca_CA
project.funder.nameRegione Lombardiaca_CA
project.funder.nameUniversitat Jaume Ica_CA
project.funder.nameGeneralitat Valencianaca_CA
project.funder.nameLaserlab–Europeca_CA
oaire.awardNumberPID2019-110927RB-I00 / AEI / 10.13039/501100011033ca_CA
oaire.awardNumberNEWMED, POR FESR 2014-2020ca_CA
oaire.awardNumberUJIB2018-68ca_CA
oaire.awardNumberPROMETEO/2020/029, ACIF/2019/019ca_CA
oaire.awardNumber654148, Horizon 2020, CUSBO002482ca_CA


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