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The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI\u2010based numerical models of the axillary region and share them with the scientific community, through an open\u2010access repository.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K\u2010means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open\u2010access repository of numerical axillary region numerical models which can be used for electromagnetic simulations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5\u00a0GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15143","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T11:11:02Z","timestamp":1627902662000},"page":"5974-5990","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Development of MRI\u2010based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imaging"],"prefix":"10.1002","volume":"48","author":[{"given":"Daniela M.","family":"Godinho","sequence":"first","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica Faculdade de Ci\u00eancias da Universidade de Lisboa Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o M.","family":"Fel\u00edcio","sequence":"additional","affiliation":[{"name":"Centro de Investiga\u00e7\u00e3o Naval (CINAV) Escola Naval Almada Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es Instituto Superior T\u00e9cnico Universidade de Lisboa Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiago","family":"Castela","sequence":"additional","affiliation":[{"name":"Departamento de Radiologia Hospital da Luz Lisboa Luz Sa\u00fade Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuno A.","family":"Silva","sequence":"additional","affiliation":[{"name":"Hospital da Luz Learning Health Luz Sa\u00fade Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria de Lurdes","family":"Orvalho","sequence":"additional","affiliation":[{"name":"Departamento de Radiologia Hospital da Luz Lisboa Luz Sa\u00fade Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos A.","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es Instituto Superior T\u00e9cnico Universidade de Lisboa Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel C.","family":"Concei\u00e7\u00e3o","sequence":"additional","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica Faculdade de Ci\u00eancias da Universidade de Lisboa Lisbon Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"The Global Cancer Observatory \u2010 World Health Organization.Breast cancer fact sheets.2020. 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