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題名 | 超音波溫度場之類神經網路模型=Modeling of Ultrasound Temperature Distribution with Artificial Neural Network |
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作 者 | 陳永耀; 謝銘鈞; 顏家鈺; 陳啟鴻; 林文澧; | 書刊名 | 中華醫學工程學刊 |
卷期 | 17:4 1997.12[民86.12] |
頁次 | 頁249-255 |
分類號 | 410.1644 |
關鍵詞 | 超音波高溫腫瘤治療; 換能器參數; 血流量; 溫度場; 類神經網路; Ultrasound hyperthermia; Transducer parameters; Perfusion; Temperature distribution; Neural networks; |
語文 | 中文(Chinese) |
中文摘要 | 超音波用來做高溫腫瘤治療時,必需能有效控制其所形成的溫度場, 方有可能達 到治療目的而不傷害到正常組織。然而超音波換能器參數與溫度場的函數關係相當複雜,利 用波場壓力方程式和生醫熱傳方程式來計算溫度場是非常耗時的,無論是做療程規劃或做溫 度與系統控制都不是很適合。本研究中,嘗試利用類神經網路的函數逼近能力來解決這個問 題。 所使用換能器為二同心之外環與內圓盤,外環內徑為 0.75 公分,外徑為 1.5 公分, 而內圓盤直徑為 0.75 公分,所用之頻率為 1.0MHz, 類神經網路模型中,輸入層為換能器 之最大振動速度與相位,而輸出層為加熱組織所形成之溫度場。此類神經網路模型經訓練與 測試後,結果顯示推測溫度值與實際計算值相當接近,且所需時間相當短。 |
英文摘要 | Before we can appropriately employ the ultrasound energy to kill the tumor and not to overheat the normal tissue by using the hyperthermia modality, the temperature distribution gnerated by the ultrasound transducer must be known. However, the relationship between the transducer parameters and the temperature distribution is rather complicated. Using the acoustic pressure and bioheat transfer equations to calculate the ultrasound power deposition and temperature distribution is very time consuming, and hence it is not appropriate for the treatment planning and the system temperature control during the treatment. In this paper, artificial neural networks are used to learn the relationship between the transducer parameters (and blood perfusion) and the tissue temperature distribution. The transducer used is a twoelement annular array with an inner disk with diameter 0.75 cm, and an outer ring with 0.75 and 1.5 cm for inner and outer diameters, respectively; and the driving frequency is 1.0 MHz. The input layer for the neural network is the maximum velocities and the relative phase difference of the transducer elements, and the output layer is the tissue temperature distribution. Results show that that the temperatures predicted by the neural networks are rather close to those caluclated by using the acoustic pressure and bioheat transfer equations, and that the computing time for the neural networks in much shorter. |
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