A BIM-Based Algorithm for Quantitative Monitoring of Temperature Distribution During Breast Hyperthermia Treatments

• Published in: IEEE access Vol. 11; pp. 38680 - 38695
• Main Authors: Onal, Hulusi, Yilmaz, Tuba, Akinci, Mehmet Nuri
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Breast cancer; Breast imaging; Dielectric properties; electromagnetic inverse scattering; Fever; Hyperthermia; Image reconstruction; Iterative methods; Microwave imaging; Monitoring; Necrosis; Noise levels; Regularization; S parameters; Signal to noise ratio; Temperature distribution; Temperature measurement; temperature monitoring; Temperature sensors; Tumors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3253482

Microwave hyperthermia (MH) treatment for breast cancer is a research interest due to its capability to initiate cell necrosis in malignant tumor or to enhance the effect of other treatment modalities such as chemotherapy. The goal of MH treatment is to increase temperature of malignant tumor up to <inline-formula> <tex-math notation="LaTeX">45^{\circ} \text{C} </tex-math></inline-formula> based on the treatment plan; however, microwave energy focusing is a challenging problem and may cause unwanted hotspots on healthy tissues; therefore, there is a need to monitor the temperature. In this paper, an iterative differential microwave imaging algorithm for temperature monitoring is presented. The algorithm is based on Born iterative method (BIM) and Tikhonov regularization. Feasibility of the algorithm is shown by a large computational study using realistic digital breast phantoms via TMz polarized 2-D scattered fields. Also, some results are given for calibrated scattering parameters, which are obtained from both a 3-D electromagnetic simulation program and a simple measurement setup. An approach for selection of matching medium in hyperthermia monitoring applications is also presented. The reconstructions are performed with scattered field data collected at 11 discrete frequency points uniformly taken from the 0.5-1.5 GHz range. For a specific heating scenario in 2-D problem, reconstruction error is lower than 0.3% with ±10% noise on reference dielectric property distribution and 40 dB signal to noise ratio (SNR). The results show that the proposed approach provides up to <inline-formula> <tex-math notation="LaTeX">3^{\circ} \text{C} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">0.1^{\circ} \text{C} </tex-math></inline-formula> resolution in temperature estimation with ±10% noise on reference dielectric property distribution for 30 dB and 60 dB SNR values, respectively.