{"id":{"repo_id":"manitoba","oai_identifier":"oai:mspace.lib.umanitoba.ca:1993/39308"},"canonical_url":"https://search.dev.ndltd.org/etd/manitoba/oai:mspace.lib.umanitoba.ca:1993/39308","repository":{"repo_id":"manitoba","name":"University of Manitoba","base_url":"https://mspace.lib.umanitoba.ca/oai/request"},"display":{"title":"Enhanced physics modelling and multistatic data integration for breast tissue reconstruction in microwave radar imaging","abstract":"Microwave breast imaging offers a low-cost, portable, and non-ionizing alternative for breast cancer detection by exploiting the contrast in dielectric properties between malignant and healthy tissues. Its simplicity and safety make it particularly attractive for screening in under-served or remote communities where conventional imaging modalities may be unavailable. However, three core challenges have limited its diagnostic performance: (1) standard Delay-and-Sum (DAS) beamformers assume a homogeneous medium and ignore tissue heterogeneity; (2) they neglect the frequency dependence of dielectric properties; and (3) true propagation speeds in complex breast tissues are unknown and must be estimated. Moreover, existing image-quality assessments rely on single-pixel contrast and localization metrics, offering little guidance for algorithmic refinement. This thesis addresses these gaps through three methodologies. Firstly, an enhanced-physics beamforming algorithm was developed as a combination of an analytical binary-partitioning model with a full frequency-dependent propagation-speed formulation, yielding piecewise time delays. The enhanced physics modelling was observed to improve image quality. Secondly, a robust, sinogram-based boundary detection algorithm was proposed to extract realistic breast outlines from raw data from the Vector Network Analyzer (VNA), replacing idealized circular models and allowing for a boundary-aware beamforming and skin suppression for the differential imaging. Thirdly, a phase-based technique of experimental propagation speed extraction was developed using the multistatic microwave data.","abstract_html":"Microwave breast imaging offers a low-cost, portable, and non-ionizing alternative for breast cancer detection by exploiting the contrast in dielectric properties between malignant and healthy tissues. Its simplicity and safety make it particularly attractive for screening in under-served or remote communities where conventional imaging modalities may be unavailable. However, three core challenges have limited its diagnostic performance: (1) standard Delay-and-Sum (DAS) beamformers assume a homogeneous medium and ignore tissue heterogeneity; (2) they neglect the frequency dependence of dielectric properties; and (3) true propagation speeds in complex breast tissues are unknown and must be estimated. Moreover, existing image-quality assessments rely on single-pixel contrast and localization metrics, offering little guidance for algorithmic refinement. This thesis addresses these gaps through three methodologies. Firstly, an enhanced-physics beamforming algorithm was developed as a combination of an analytical binary-partitioning model with a full frequency-dependent propagation-speed formulation, yielding piecewise time delays. The enhanced physics modelling was observed to improve image quality. Secondly, a robust, sinogram-based boundary detection algorithm was proposed to extract realistic breast outlines from raw data from the Vector Network Analyzer (VNA), replacing idealized circular models and allowing for a boundary-aware beamforming and skin suppression for the differential imaging. Thirdly, a phase-based technique of experimental propagation speed extraction was developed using the multistatic microwave data.","abstract_has_math":false,"creators":["Prykhodko, Illia"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Pistorius, Stephen"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-26","date_published":"2025-08-26","updated_at":"2026-08-21T22:21:56Z","subjects":["University of Manitoba","microwave imaging","radar imaging","microwave radar","medical physics","medical imaging","physics modelling","boundary detection","raytracing","propagation speed","differential imaging","skin suppresion","skin alignment"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1993/39308","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://mspace.lib.umanitoba.ca/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Amspace.lib.umanitoba.ca%3A1993%2F39308","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.supervisor","label":"Supervisor","values":["Pistorius, Stephen"]},{"key":"dc:creator","label":"Author","values":["Prykhodko, Illia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-08T16:13:20Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-08T16:13:20Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-26"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["University of Manitoba","microwave imaging","radar imaging","microwave radar","medical physics","medical imaging","physics modelling","boundary detection","raytracing","propagation speed","differential imaging","skin suppresion","skin alignment"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1993/39308"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Microwave breast imaging offers a low-cost, portable, and non-ionizing alternative for breast cancer detection by exploiting the contrast in dielectric properties between malignant and healthy tissues. Its simplicity and safety make it particularly attractive for screening in under-served or remote communities where conventional imaging modalities may be unavailable. However, three core challenges have limited its diagnostic performance: (1) standard Delay-and-Sum (DAS) beamformers assume a homogeneous medium and ignore tissue heterogeneity; (2) they neglect the frequency dependence of dielectric properties; and (3) true propagation speeds in complex breast tissues are unknown and must be estimated. Moreover, existing image-quality assessments rely on single-pixel contrast and localization metrics, offering little guidance for algorithmic refinement. This thesis addresses these gaps through three methodologies. Firstly, an enhanced-physics beamforming algorithm was developed as a combination of an analytical binary-partitioning model with a full frequency-dependent propagation-speed formulation, yielding piecewise time delays. The enhanced physics modelling was observed to improve image quality. Secondly, a robust, sinogram-based boundary detection algorithm was proposed to extract realistic breast outlines from raw data from the Vector Network Analyzer (VNA), replacing idealized circular models and allowing for a boundary-aware beamforming and skin suppression for the differential imaging. Thirdly, a phase-based technique of experimental propagation speed extraction was developed using the multistatic microwave data."]},{"key":"dc:title","label":"Title","values":["Enhanced physics modelling and multistatic data integration for breast tissue reconstruction in microwave radar imaging"]}]}],"canonical_facts":{"dc:contributor.supervisor":["Pistorius, Stephen"],"dc:creator":["Prykhodko, Illia"],"dc:date.accessioned":["2025-09-08T16:13:20Z"],"dc:date.available":["2025-09-08T16:13:20Z"],"dc:date.issued":["2025-08-26"],"dc:description.abstract":["Microwave breast imaging offers a low-cost, portable, and non-ionizing alternative for breast cancer detection by exploiting the contrast in dielectric properties between malignant and healthy tissues. Its simplicity and safety make it particularly attractive for screening in under-served or remote communities where conventional imaging modalities may be unavailable. However, three core challenges have limited its diagnostic performance: (1) standard Delay-and-Sum (DAS) beamformers assume a homogeneous medium and ignore tissue heterogeneity; (2) they neglect the frequency dependence of dielectric properties; and (3) true propagation speeds in complex breast tissues are unknown and must be estimated. Moreover, existing image-quality assessments rely on single-pixel contrast and localization metrics, offering little guidance for algorithmic refinement. This thesis addresses these gaps through three methodologies. Firstly, an enhanced-physics beamforming algorithm was developed as a combination of an analytical binary-partitioning model with a full frequency-dependent propagation-speed formulation, yielding piecewise time delays. The enhanced physics modelling was observed to improve image quality. Secondly, a robust, sinogram-based boundary detection algorithm was proposed to extract realistic breast outlines from raw data from the Vector Network Analyzer (VNA), replacing idealized circular models and allowing for a boundary-aware beamforming and skin suppression for the differential imaging. Thirdly, a phase-based technique of experimental propagation speed extraction was developed using the multistatic microwave data."],"dc:identifier.uri":["http://hdl.handle.net/1993/39308"],"dc:language.iso":["eng"],"dc:subject":["University of Manitoba","microwave imaging","radar imaging","microwave radar","medical physics","medical imaging","physics modelling","boundary detection","raytracing","propagation speed","differential imaging","skin suppresion","skin alignment"],"dc:title":["Enhanced physics modelling and multistatic data integration for breast tissue reconstruction in microwave radar imaging"]},"updated_at":"2026-08-21T22:21:56Z"}