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Cross-modality Lossless Compression of PET-CT Images

2021-02Comunicação em conferência Acesso restrito
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorParracho, João O.
dc.contributor.authorThomaz, Lucas A.
dc.contributor.authorTávora, Luís M. N.
dc.contributor.authorAssunção, Pedro A. A.
dc.contributor.authorFaria, Sérgio M. M.
dc.date.accessioned2026-03-24T14:54:56Z
dc.date.available2026-03-24T14:54:56Z
dc.date.issued2021-02
dc.descriptionEISBN - 978-1-6654-1588-0
dc.descriptionDate of Conference: 11-12 February 2021
dc.description.abstractThe huge amount of data resulting from the acquisition of medical images with multiple modalities can be overwhelming for storage and sharing through communication systems. Thus, efficient compression algorithms must be introduced to reduce the burden of storage and communication resources required by such amount of data. However, since in the medical context all details are important, the adoption of lossless image compression algorithms is paramount. This paper proposes a novel lossless compression scheme tailored to jointly compress the modality of computerized tomography (CT) and that of positron emission tomography (PET). Different approaches are adopted, namely image-to-image translation techniques and redundancies between both images are also explored. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Then, the residue that results from the differences between the original PET and its estimation is also compressed. Thus, instead of compressing two independent image modalities, i.e., both images of the original PET-CT pair, in the proposed approach only the CT is independently encoded along with the PET residue. The performed experiments using a publicly available PET-CT pair dataset show that the proposed scheme attains up to 8.9 % compression gains for the PET data, in comparison with the naive approach, and up to 3.5 % gains for the PET-CT pair.eng
dc.description.sponsorshipThis work was supported by the Programa Operacional Regional do Centro, project PlenoISLA POCI-01-0145-FEDER-028325 and by FCT/MCTES through national funds and when applicable co-funded by EU funds under the project UIDB/EEA/50008/2020.
dc.identifier.citationJ. O. Parracho, L. A. Thomaz, L. M. N. Távora, P. A. A. Assunção and S. M. M. Faria, "Cross-modality Lossless Compression of PET-CT Images," 2021 Telecoms Conference (ConfTELE), Leiria, Portugal, 2021, pp. 1-6, doi: https://doi.org/10.1109/ConfTELE50222.2021.9435467.
dc.identifier.doi10.1109/conftele50222.2021.9435467
dc.identifier.isbn978-1-6654-4680-8
dc.identifier.isbn978-1-6654-1588-0
dc.identifier.urihttp://hdl.handle.net/10400.8/15968
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/9435467
dc.relation.ispartof2021 Telecoms Conference (ConfTELE)
dc.rights.uriN/A
dc.subjectComputerized tomography
dc.subjectpositron emission tomography
dc.subjectHEVC
dc.subjectGenerative Adversarial Network
dc.subjectimage-toimage translation
dc.titleCross-modality Lossless Compression of PET-CT Imageseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2021-02
oaire.citation.conferencePlaceLeiria, Portugal
oaire.citation.title2021 Telecoms Conference, ConfTELE 2021
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameOliveira Parracho
person.familyNameThomaz
person.familyNamede Oliveira Pegado de Noronha E Távora
person.familyNameAssunção
person.familyNameFaria
person.givenNameJoão
person.givenNameLucas
person.givenNameLuís Miguel
person.givenNamePedro
person.givenNameSergio
person.identifier.ciencia-id121C-FADA-D750
person.identifier.ciencia-id6811-3984-C17B
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0003-2821-9154
person.identifier.orcid0000-0002-1004-7772
person.identifier.orcid0000-0002-8580-1979
person.identifier.orcid0000-0001-9539-8311
person.identifier.orcid0000-0002-0993-9124
person.identifier.ridA-4827-2017
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id6701838347
person.identifier.scopus-author-id14027853900
relation.isAuthorOfPublication51d7b855-4d05-4f21-a8ca-7230dc0a647c
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relation.isAuthorOfPublicationf69bd4d6-a6ef-4d20-8148-575478909661
relation.isAuthorOfPublication.latestForDiscovery51d7b855-4d05-4f21-a8ca-7230dc0a647c

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The huge amount of data resulting from the acquisition of medical images with multiple modalities can be overwhelming for storage and sharing through communication systems. Thus, efficient compression algorithms must be introduced to reduce the burden of storage and communication resources required by such amount of data. However, since in the medical context all details are important, the adoption of lossless image compression algorithms is paramount. This paper proposes a novel lossless compression scheme tailored to jointly compress the modality of computerized tomography (CT) and that of positron emission tomography (PET). Different approaches are adopted, namely image-to-image translation techniques and redundancies between both images are also explored. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Then, the residue that results from the differences between the original PET and its estimation is also compressed. Thus, instead of compressing two independent image modalities, i.e., both images of the original PET-CT pair, in the proposed approach only the CT is independently encoded along with the PET residue. The performed experiments using a publicly available PET-CT pair dataset show that the proposed scheme attains up to 8.9 % compression gains for the PET data, in comparison with the naive approach, and up to 3.5 % gains for the PET-CT pair.
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