Curriculum Vitaes

Essam Rashed

  (ラシド イサム)

Profile Information

Affiliation
Professor, Graduate School of Information Science, University of Hyogo
Degree
Ph.D.(University of Tsukuba)

Researcher number
60837590
ORCID ID
 https://orcid.org/0000-0001-6571-9807
J-GLOBAL ID
202101013772964054
Researcher ID
F-4320-2012
researchmap Member ID
R000022998

External link

Awards

 20

Papers

 112
  • Mennatullah Mahmoud, Mohammad Mansour, Hisham M. Elrefai, Amira J. Hamed, Essam A. Rashed
    Biomedical Signal Processing and Control, Aug, 2025  Peer-reviewedLast author
  • Essam A. Rashed, Mohammad Al-Shatouri, Ilkka Laakso, Sachiko Kodera, Akimasa Hirata
    Biomedical Signal Processing and Control, 104 107481, Jun, 2025  Peer-reviewedLead authorCorresponding author
  • Sachiko Kodera, Reina Yoshida, Essam A Rashed, Yinliang Diao, Hiroyuki Takizawa, Akimasa Hirata
    Physics in Medicine & Biology, 70(65013), Mar 11, 2025  Peer-reviewed
    Abstract 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.
  • Ahmed T. Elboardy, Ziad Elshaer, Ghada Khoriba, Tamer Arafa, Essam A. Rashed
    The 22nd IEEE International Conference on Learning and Technology (L&S25), Jan, 2025  Peer-reviewedLast author
  • Ahmed Soliman, Yalda Zafari-Ghadim, Yousif Yousif, Ahmed Ibrahim, Amr Mohamed, Essam A. Rashed, Mohamed Mabrok
    IEEE Access, 1-1, Dec, 2024  Peer-reviewed

Misc.

 4

Books and Other Publications

 1

Professional Memberships

 3

Research Projects

 7