LEE- Telekomünikasyon Mühendisliği Lisansüstü Programı

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http://hdl.handle.net/11527/19468

Gözat

Yazar "Altıntaş Yıldız, Gülşah" ile LEE- Telekomünikasyon Mühendisliği Lisansüstü Programı'a göz atma
  • Öge
    A novel antenna configuration for microwave hyperthermia
    (Graduate School, 2022-11-28) Altıntaş Yıldız, Gülşah ; Akduman, İbrahim ; Abdulsabeh Yılmaz, Tuğba ; 504182309 ; Telecommunications Engineering
    Breast cancer affects approximately 2.5 million women each year and the consequences can be fatal. When treated correctly, however, the survival rates are very high. Surgical operation such as lumpectomy or mastectomy are invasive techniques that remove the partial or the whole breast. With early diagnosed cancers and the post-surgical patients, the most used therapy techniques are the radiotherapy, chemotherapy and the use of other anti-cancer agents. The economic and the psychological repercussions may be minimized by the increase efficiency of the treatments. It has been shown that with the artificial hyperthermia, elevated temperature levels at the cancer regions, the effectiveness of these modalities increase. Microwave breast hyperthermia (MH) aims to increase the temperature at the tumor location over its normal levels. During the procedure, the unwanted heated regions called hotspots can occur. The main aim of the MH is to prevent the hotspots while obtaining the necessary temperature at the tumor. Absorbed heat energy per kilogram at the breast, specific absorption rate (SAR), needs to be adjusted for a controlled MH. The choice of the MH applicator design is important for a superior energy focus on the target. Although hyperthermia treatment planning (HTP) changes for every patient, the MH applicator is required to be effective for different breast models and tumor types. In the first part of the thesis, the linear antenna arrays are implemented as MH applicators. We presented the focusing maps as an application guide for MH focusing by adjusting the antenna phase values. Furthermore, these focusing maps put forward the basic principle of focusing the energy at the breast. Sub-grouping the antenna, we obtained two phase main parameters that control the horizontal and vertical focus change. By adjusting these two phase values, we could focus the energy onto the target locations and we showed that with this simple structure, there is no need for optimization methods. However, the linear applicator performance was not successful for some target points, especially when the target is far away from both of the arrays. In the second part of the thesis, we improved the linear MH applicator. We concluded that the reason for the low performance of the linear applicator is mainly due to non-symmetrical geometry of the applicator and the resulting poor coverage. we proposed to radially re-adjust the position of the linear applicator for a better focusing ability while fixing the breast phantom. This generates multiple different applicator scheme without actually changing the applicator design. Particle swarm optimization (PSO) method is used for antenna excitation parameter selection. For the examined two targets, 135° rotated linear applicator gave 35-84% higher T BRS and 21-28% higher T BRT values than the fixed linear applicator, where T BRS stands for the target-to-breast SAR ratio and T BRT stands for the target-to-breast temperature ratio. Not only the rotated linear applicator gave higher performance, but also the circular array is rotated and the results were improved for one target. One of the main results of this study is that, for one target, the rotated linear applicator gave better results than the circular array, which is the state of the art. For the deep-seated target, 135° rotated linear applicator has 80% higher T BRS and 59% higher T BRT than the circular applicator with the same number of antennas. For the other target, the results of the linear and circular were comparable. However, the results obtained with the PSO were not robust. With different initial values (random in our study), the results were very different from each other, and we did 10 repetitions and took the best performing results. In the third part of the thesis, we presented deep-learning based antenna excitation parameter selection method. This method utilizes the learning ability of convolutional neural networks (CNN), rather than searching the solution space from random initial values as PSO does. The data set for CNN training was collected by superposing the electric fields obtained from individual antenna elements. We implemented a realistic breast phantom with and without a tumor inclusion. We used linear and circular applicators to validate the method. CNNs were trained offline with the data sets created first for the phase and then for the amplitude of the antennas. A mask of 1s and 0s is used to define the target region to be focused. This mask was given as the input to CNN models, and the corresponding phase and the amplitude values are calculated within seconds from the CNN models. The proposed approach outperforms the look-up table results, as the phase-only optimization and phase–power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator
  • Öge
    A roadmap for breast cancer microwave hyperthermia treatment planning and experimental systems
    (Graduate School, 2024-07-04) Şafak, Meltem Duygu ; Altıntaş Yıldız, Gülşah ; 504191326 ; Telecommunications Engineering
    Breast cancer affects approximately 2.5 million women each year and can be fatal if not treated correctly. However, with proper treatment, survival rates are very high. Common treatments include invasive surgical procedures such as lumpectomy or mastectomy, and non-surgical methods like radiotherapy, chemotherapy, and other anti-cancer agents. Enhancing the efficiency of these treatments can mitigate the economic and psychological impacts on patients. Studies have shown that artificial hyperthermia, which involves elevating the temperature in cancerous regions, can enhance the effectiveness of these modalities. Microwave breast hyperthermia (MH) aims to raise the temperature at the tumor site above normal levels. During this procedure, unwanted hotspots can occur, and the main goal of MH is to avoid these while achieving the necessary temperature at the tumor. The specific absorption rate (SAR), which measures the absorbed heat energy per kilogram of breast tissue, needs to be carefully controlled. The design of the MH applicator is crucial for focusing energy on the target effectively. Despite variations in hyperthermia treatment planning (HTP) for each patient, the MH applicator must be effective across different breast models and tumor types. The optimization and predictive modeling of temperature-dependent dielectric properties in microwave hyperthermia treatments, focusing primarily on breast cancer is investigated. This research aims to enhance the efficacy and precision of hyperthermia therapy through a combination of computational simulations, empirical data analysis, and deep learning techniques. This study is a comprehensive exploration of microwave hyperthermia treatment planning for breast cancer, focusing particularly on the critical consideration of temperature-dependent dielectric properties (TD-DP) within this context. In addition, an experimental study was conducted to realize computational analysis. It delves into multifaceted aspects of microwave hyperthermia treatment, spanning from the optimization of antenna parameters to the prediction of electromagnetic distribution through innovative methodologies like the U-Net architecture. One of the central inquiries is the optimization of antenna parameters concerning temperature-dependent dielectric properties. This study delves into the intricacies of how variations in these properties can influence treatment outcomes and efficacy. By analyzing these relationships, this thesis aims to establish optimized antenna configurations that maximize treatment precision and effectiveness. Deep learning, particularly convolutional neural networks (CNN), emerges as a powerful tool within this framework. By leveraging CNNs, this thesis investigates methods to use as a preliminary step of hyperthermia antenna excitation parameter selection. This integration of cutting-edge artificial intelligence techniques holds promise for streamlining and automating aspects of treatment planning, thereby potentially reducing human error and enhancing overall efficiency. Particularly, the U-Net model's potential is studied in automating the generation of electric field distribution of a particular dielectric distribution such as the breast tissue. By harnessing the capabilities of artificial intelligence, particularly in image analysis and processing, it aims to develop more robust and efficient methodologies for treatment planning. The integration of the U-Net model represents a significant advancement in this regard, promising to streamline processes and enhance treatment precision. To verify the performed computational simulations, an experimental microwave hyperthermia system was built. A circular array of 12 dipole antennas was installed in this system to experiment on tissue-mimicking phantom to gather information on microwave hyperthermia treatment system. Therefore, a significant amount of information on microwave hyperthermia is gathered through this experiment. Ultimately, the overarching objective of the thesis is to advance microwave hyperthermia treatment planning for breast cancer by improving both precision and efficacy. By synthesizing insights from diverse disciplines such as electromagnetics and deep learning, this thesis seeks to push the boundaries of current practices and pave the way for more effective treatment strategies. Through its meticulous analysis and innovative approaches, the thesis contributes valuable knowledge and methodologies to the ongoing quest for improved cancer therapies. To achieve that, COMSOL Multiphysics software is utilized to simulate the electromagnetic and thermal behavior of breast tissue during hyperthermia treatment. These simulations consider both constant and temperature-dependent dielectric properties. Empirical data is collected using phantoms that mimic the dielectric properties of breast tissue. Temperature distributions are recorded and compared with simulated results to validate the models. U-Net architecture, an encoder-decoder model, is used to predict electromagnetic field distributions, significantly reducing the computational workload and enhancing the accuracy of treatment planning. This research underscores the importance of optimizing antenna configurations to achieve targeted heating while minimizing damage to surrounding healthy tissues. Variations in tissue properties with temperature are crucial for effective hyperthermia treatment, and modeling these changes can lead to better treatment protocols. Despite the promising results, the transition of high-precision hyperthermia into clinical practice faces challenges such as technical complexities, high computational costs, and the need for further validation and optimization. Future research should focus on overcoming the remaining technical and computational barriers, refining the proposed methods, and conducting extensive validation studies to facilitate the clinical adoption of high-precision hyperthermia treatments. This thesis represents a significant step towards improving the precision and effectiveness of hyperthermia therapy, offering a comprehensive framework for future advancements in this field.