Publication
Model-based deep learning framework for accelerated optical projection tomography
| dc.contributor.author | Obando, Marcos | |
| dc.contributor.author | Bassi, Andrea | |
| dc.contributor.author | Ducros, Nicolas | |
| dc.contributor.author | Mato, Germán | |
| dc.contributor.author | Correia, Teresa | |
| dc.date.accessioned | 2024-02-03T13:24:41Z | |
| dc.date.available | 2024-02-03T13:24:41Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | In this work, we propose a model-based deep learning reconstruction algorithm for optical projection tomography (ToMoDL), to greatly reduce acquisition and reconstruction times. The proposed method iterates over a data consistency step and an image domain artefact removal step achieved by a convolutional neural network. A preprocessing stage is also included to avoid potential misalignments between the sample center of rotation and the detector. The algorithm is trained using a database of wild-type zebrafish (Danio rerio) at different stages of development to minimise the mean square error for a fixed number of iterations. Using a cross-validation scheme, we compare the results to other reconstruction methods, such as filtered backprojection, compressed sensing and a direct deep learning method where the pseudo-inverse solution is corrected by a U-Net. The proposed method performs equally well or better than the alternatives. For a highly reduced number of projections, only the U-Net method provides images comparable to those obtained with ToMoDL. However, ToMoDL has a much better performance if the amount of data available for training is limited, given that the number of network trainable parameters is smaller. | pt_PT |
| dc.description.sponsorship | LCF/PR/HR22/00533; Grant agreement no.101094250; WT 203148/Z/16/Z | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1038/s41598-023-47650-3 | pt_PT |
| dc.identifier.issn | 2045-2322 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/20365 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Nature Portfolio | pt_PT |
| dc.relation | Algarve Centre for Marine Sciences | |
| dc.relation | Algarve Centre for Marine Sciences | |
| dc.relation | Centre for Marine and Environmental Research | |
| dc.relation | OPTical Imaging of Molecular and signalling Activity in Real-time: application to flatfish metamorphosis | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Algorithms | pt_PT |
| dc.title | Model-based deep learning framework for accelerated optical projection tomography | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Algarve Centre for Marine Sciences | |
| oaire.awardTitle | Algarve Centre for Marine Sciences | |
| oaire.awardTitle | Centre for Marine and Environmental Research | |
| oaire.awardTitle | OPTical Imaging of Molecular and signalling Activity in Real-time: application to flatfish metamorphosis | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0101%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/867450/EU | |
| oaire.citation.issue | 1 | pt_PT |
| oaire.citation.startPage | 21735 | pt_PT |
| oaire.citation.title | Scientific Reports | pt_PT |
| oaire.citation.volume | 13 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | H2020 | |
| person.familyName | Correia | |
| person.givenName | Teresa | |
| person.identifier.ciencia-id | F01E-082A-5B36 | |
| person.identifier.orcid | 0000-0002-1606-9550 | |
| person.identifier.scopus-author-id | 23392190500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | European Commission | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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