研究生: |
史安琪 Shi, An-Qi |
---|---|
論文名稱: |
解析式構建展開布拉格峰方法的分析與參數選取的研究 Parametric studies on various methodologies for creating spread-out Bragg peaks in proton therapy |
指導教授: |
許榮鈞
Sheu, Rong-Jiun |
口試委員: |
蔡惠予
Tsai, Hui-Yu 許芳裕 Hsu, Fang-Yuh 林宗逸 Lin, Tzung-Yi |
學位類別: |
碩士 Master |
系所名稱: |
原子科學院 - 核子工程與科學研究所 Nuclear Engineering and Science |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 45 |
中文關鍵詞: | 質子治療 、展開的布拉格峰 |
外文關鍵詞: | Proton therapy, Spread Out Bragg Peak |
相關次數: | 點閱:50 下載:0 |
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本研究旨在重新評估用於生成平坦的展寬的布拉格曲綫(SOBP)的權重函數,並探討不同方法對SOBP平坦度的影響。研究使用FLUKA模擬質子與水的相互作用,生成的布拉格質子曲綫。首先,回顧了幾篇關於SOBP權重函數的文獻,討論其實施方法。Bortfeld和Schlegel於1996年提出的權重函數是當前所有權重函數的基礎,但因忽略了通量減少和range straggling的影響,導致應用於模擬的數據時,生成的SOBP成傾斜的。接著我們實現了Jette和Chen在2011提出的方法,透過擬合關於射程特性參數p獲得平坦的SOBP。本研究對與FLUKA模擬的布拉格曲綫的最佳參數p值進行了探討。另一種方法由Wang等人在2019年提出,重新擬合函數以平坦化SOBP,本研究對此方法進行了實現和對其中的參數進行了研究。最後對蒙特卡羅计算與矩阵计算相結合的方法(MCMC)進行了實現和比較。總結來說,選擇適當的權重函數對於生成平坦的SOBP至關重要,不同的方法可能導致略有不同的結果,因此需要根據實際情況選擇最適合的方法。
The aim of this study is to reevaluate the weight function used to generate a flat broadened Bragg curve (SOBP) and explore the influence of different methods on the flatness of SOBP. FLUKA was used to simulate the interaction between protons and water, and the Bragg proton curve was generated. Firstly, several literatures about SOBP weight function are reviewed and its implementation methods are discussed. The weight function proposed by Bortfeld and Schlegel in 1996 is the basis of the current ownership heavy function, but because it ignores the influence of flux reduction and range straggling, the generated SOBP is skewed when applied to the simulated data. We then implement the method proposed by Jette and Chen in 2011 to obtain a flat SOBP by fitting the parameter p about the range characteristics. In this paper, the optimum parameter P-value of Bragg curve simulated with FLUKA is discussed. Another method, proposed by Wang et al in 2019, is to refit the function to flatter SOBP. This method is implemented and its parameters are studied in this study. Finally, the method of combining Monte Carlo computation with matrix computation (MCMC) is realized and compared. In summary, choosing the appropriate weight function is crucial for generating a flat SOBP, and different methods can lead to slightly different results, so you need to choose the most suitable method according to the actual situation.
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