In this paper, we present an MWI approach for neck cancer hyperthermia treatment monitoring. In this kind of treatment, it is essential to avoid unwanted heating of the spinal cord, as this is a particularly sensitive area. To pursue this goal, a processing framework in which a traditional MWI algorithm cooperates with a deep-learning framework has been developed, in order to achieve a real-time and reliable assessment of the temperature of the spinal cord during the treatment. In the proposed architecture, a microwave image formed from the processing of the raw data acquired by the MWI device is supplied to a neural network, which has the task of classifying it into either of two categories (“heated” or “unheated”).
Deep Learning Enhanced Microwave Imaging for Hyperthermia Treatment Monitoring / Yago Ruiz, Á.; Prokhorova, A.; Cavagnaro, M.; Helbig, M.; Crocco, L.. - (2022). (Intervento presentato al convegno 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting tenutosi a Denver - USA).
Deep Learning Enhanced Microwave Imaging for Hyperthermia Treatment Monitoring
M. Cavagnaro;
2022
Abstract
In this paper, we present an MWI approach for neck cancer hyperthermia treatment monitoring. In this kind of treatment, it is essential to avoid unwanted heating of the spinal cord, as this is a particularly sensitive area. To pursue this goal, a processing framework in which a traditional MWI algorithm cooperates with a deep-learning framework has been developed, in order to achieve a real-time and reliable assessment of the temperature of the spinal cord during the treatment. In the proposed architecture, a microwave image formed from the processing of the raw data acquired by the MWI device is supplied to a neural network, which has the task of classifying it into either of two categories (“heated” or “unheated”).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
