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Microwave Imaging to Improve Breast Cancer Diagnosis

2022-07Tese de doutoramento Acesso aberto
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datacite.subject.fosCiências Médicas::Biotecnologia Médicapt_PT
dc.contributor.advisorConceição, Raquel Cruz da
dc.contributor.advisorFernandes, Carlos António Cardoso
dc.contributor.authorGodinho, Daniela M.
dc.date.accessioned2023-01-03T15:39:25Z
dc.date.available2023-01-03T15:39:25Z
dc.date.issued2022-07
dc.date.submitted2022-03
dc.description.abstractBreast cancer is the most prevalent type of cancer worldwide. The correct diagnosis of Axillary Lymph Nodes (ALNs) is important for an accurate staging of breast cancer. The performance of current imaging modalities for both breast cancer detection and staging is still unsatisfactory. Microwave Imaging (MWI) has been studied to aid breast cancer diagnosis. This thesis addresses several novel aspects of the development of air-operated MWI systems for both breast cancer detection and staging. Firstly, refraction effects in air-operated setups are evaluated to understand whether refraction calculation should be included in image reconstruction algorithms. Then, the research completed towards the development of a MWI system to detect the ALNs is presented. Anthropomorphic numerical phantoms of the axillary region are created, and the dielectric properties of ALNs are estimated from Magnetic Resonance Imaging exams. The first pre-clinical MWI setup tailored to detect ALNs is numerically and experimentally tested. To complement MWI results, the feasibility of using machine learning algorithms to classify healthy and metastasised ALNs using microwave signals is analysed. Finally, an additional study towards breast cancer detection is presented by proposing a prototype which uses a focal system to focus the energy into the breast and decrease the coupling between antennas. The results show refraction calculation may be neglected in low to moderate permittivity media. Moreover, MWI has the potential as an imaging technique to assess ALN diagnosis as estimation of dielectric properties indicate there is sufficient contrast between healthy and metastasised ALNs, and the imaging results obtained in this thesis are promising for ALN detection. The performance of classification models shows these models may potentially give complementary information to imaging results. The proposed breast imaging prototype also shows promising results for breast cancer detection.pt_PT
dc.identifier.tid101617968pt_PT
dc.identifier.urihttp://hdl.handle.net/10451/55585
dc.language.isoengpt_PT
dc.relationAxillary Lymph Node Microwave Imaging ALN-MWI to Improve Breast Cancer Diagnosis
dc.relationInstitute of Biophysics and Biomedical Engineering (IBEB)
dc.relationInstitute of <biophysics and Biomedical Engineering
dc.relationInstituto de Telecomunicações
dc.subjectAxilapt_PT
dc.subjectCancro da Mamapt_PT
dc.subjectGânglios Linfáticospt_PT
dc.subjectImagem Médicapt_PT
dc.subjectImagem por Micro-ondaspt_PT
dc.subjectAxillary Regionpt_PT
dc.subjectBreast Cancerpt_PT
dc.subjectLymph Nodespt_PT
dc.subjectMedical Imagingpt_PT
dc.subjectMicrowave Imagingpt_PT
dc.titleMicrowave Imaging to Improve Breast Cancer Diagnosispt_PT
dc.typedoctoral thesis
dspace.entity.typePublication
oaire.awardNumberSFRH/BD/129230/2017
oaire.awardNumberUID/BIO/00645/2013
oaire.awardNumberUIDB/00645/2020
oaire.awardNumberUIDB/50008/2020
oaire.awardTitleAxillary Lymph Node Microwave Imaging ALN-MWI to Improve Breast Cancer Diagnosis
oaire.awardTitleInstitute of Biophysics and Biomedical Engineering (IBEB)
oaire.awardTitleInstitute of <biophysics and Biomedical Engineering
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F129230%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FBIO%2F00645%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00645%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.fundingStreamOE
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMarques Godinho
person.givenNameDaniela
person.identifier0D5DsyYAAAAJ
person.identifier.ciencia-idED18-EB95-1DC6
person.identifier.orcid0000-0003-4053-8887
person.identifier.scopus-author-id57201803423
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typedoctoralThesispt_PT
relation.isAuthorOfPublication2068342c-a237-4894-ac0b-f738349589e4
relation.isAuthorOfPublication.latestForDiscovery2068342c-a237-4894-ac0b-f738349589e4
relation.isProjectOfPublicationdf06c484-d4da-4895-9c14-78c044a1c3ae
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relation.isProjectOfPublication.latestForDiscovery58ca2253-fb87-4a9e-82aa-1e670bb078c7
thesis.degree.nameTese de doutoramento, Engenharia Biomédica e Biofísica, Universidade de Lisboa, Faculdade de Ciências, 2022pt_PT

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