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Med. Biol."],"published-print":{"date-parts":[[2025,3,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Computational uncertainty and variability of power absorption and temperature rise in humans for radiofrequency (RF) exposure is a critical factor in ensuring human protection. This aspect has been emphasized as a priority. However, accurately modeling head tissue composition and assigning tissue dielectric and thermal properties remains a challenging task. This study investigated the impact of segmentation-based versus segmentation-free models for assessing localized RF exposure. <jats:italic>Approach.<\/jats:italic> Two computational head models were compared: one employing traditional tissue segmentation and the other leveraging deep learning to estimate tissue dielectric and thermal properties directly from magnetic resonance images. The finite-difference time-domain method and the bioheat transfer equation was solved to assess temperature rise for local exposure. Inter-subject variability and dosimetric uncertainties were analyzed across multiple frequencies. <jats:italic>Main results.<\/jats:italic> The comparison between the two methods for head modeling demonstrated strong consistency, with differences in peak temperature rise of 7.6 \u00b1 6.4%. The segmentation-free model showed reduced inter-subject variability, particularly at higher frequencies where superficial heating dominates. The maximum relative standard deviation in the inter-subject variability of heating factor was 15.0% at 3 GHz and decreased with increasing frequencies. <jats:italic>Significance.<\/jats:italic> This study highlights the advantages of segmentation-free deep-learning models for RF dosimetry, particularly in reducing inter-subject variability and improving computational efficiency. While the differences between the two models are relatively small compared to overall dosimetric uncertainty, segmentation-free models offer a promising approach for refining individual-specific exposure assessments. These findings contribute to improving the accuracy and consistency of human protection guidelines against RF electromagnetic field exposure.<\/jats:p>","DOI":"10.1088\/1361-6560\/adb935","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:54:01Z","timestamp":1740178441000},"page":"065013","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Power absorption and temperature rise in deep learning based head models for local radiofrequency exposures"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6595-0742","authenticated-orcid":true,"given":"Sachiko","family":"Kodera","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reina","family":"Yoshida","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6571-9807","authenticated-orcid":true,"given":"Essam A","family":"Rashed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6492-4515","authenticated-orcid":true,"given":"Yinliang","family":"Diao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2858-3140","authenticated-orcid":false,"given":"Hiroyuki","family":"Takizawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8336-1140","authenticated-orcid":true,"given":"Akimasa","family":"Hirata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"pmbadb935bib1","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/TEMC.2006.873870","article-title":"Comparisons of computed mobile phone induced SAR in the SAM phantom to that in anatomically correct models of the human head","volume":"48","author":"Beard","year":"2006","journal-title":"IEEE Trans. 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