Accurate temperature monitoring remains a challenge in microwave hyperthermia (HT), in particular for deepseated tumors. Recent work has demonstrated that combining scarce temperature measurements with precomputed simulation libraries enables real-time 3D temperature reconstruction in the entire region of interest (ROI). In this study, we validate this reconstruction strategy using the standardized benchmark models released by the European Society for Hyperthermic Oncology (ESHO), strictly following all ESHO guidelines for numerical simulation and verification in hyperthermia treatment planning (HTP). Results obtained with the “Alex” head and neck benchmark model confirm the possibility to achieve an average error lower than 0.4 ° C in 95% of the ROI even when the sensor points used in the reconstruction are scarce and affected by noise.

Real-Time Temperature Reconstruction in Microwave Hyperthermia: ESHO Models Validation / Firuzalizadeh, M., Gaffoglio, R., Giordanengo, G., Righero, M., Vecchi, G.. - (2026), pp. 1-4. (20th European Conference on Antennas and Propagation (EuCAP 2026) Dublin (Ire) 19-24 April 2026) [10.23919/eucap68105.2026.11612285].

Real-Time Temperature Reconstruction in Microwave Hyperthermia: ESHO Models Validation

Firuzalizadeh, Maryam;Gaffoglio, Rossella;Giordanengo, Giorgio;Righero, Marco;Vecchi, Giuseppe
2026

Abstract

Accurate temperature monitoring remains a challenge in microwave hyperthermia (HT), in particular for deepseated tumors. Recent work has demonstrated that combining scarce temperature measurements with precomputed simulation libraries enables real-time 3D temperature reconstruction in the entire region of interest (ROI). In this study, we validate this reconstruction strategy using the standardized benchmark models released by the European Society for Hyperthermic Oncology (ESHO), strictly following all ESHO guidelines for numerical simulation and verification in hyperthermia treatment planning (HTP). Results obtained with the “Alex” head and neck benchmark model confirm the possibility to achieve an average error lower than 0.4 ° C in 95% of the ROI even when the sensor points used in the reconstruction are scarce and affected by noise.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3014852