Power absorption and temperature rise in deep learning based head models for local radiofrequency exposures

• Published in: Physics in medicine & biology Vol. 70; no. 6; pp. 65013 - 65025
• Main Authors: Kodera, Sachiko, Yoshida, Reina, Rashed, Essam A, Diao, Yinliang, Takizawa, Hiroyuki, Hirata, Akimasa
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 16.03.2025
• Subjects: Absorption, Radiation; Deep Learning; Head - diagnostic imaging; Head - radiation effects; Humans; international guidelines; local exposure; Magnetic Resonance Imaging; power absorption; Radiation Exposure; Radio Waves - adverse effects; Radiometry; Temperature; temperature rise; tissue thermal parameter; power absorption; deep learning; international guidelines; local exposure; temperature rise; tissue thermal parameter
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/adb935

Objective. 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. Approach. 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. Main results. The comparison between the two methods for head modeling demonstrated strong consistency, with differences in peak temperature rise of 7.6 ± 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. Significance. 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.