本文選題：調(diào)強(qiáng)放射治療 + 神經(jīng)網(wǎng)絡(luò)�。� 參考：《河南工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著物理學(xué)與生物科學(xué)理論的不斷完善以及放射治療技術(shù)的快速發(fā)展,放射治療已經(jīng)成為目前治療腫瘤的主要手段之一。為了提高治療效果,降低射線對(duì)人體的副作用,在對(duì)病人實(shí)施放射治療時(shí),需要精準(zhǔn)地控制照射野的劑量分布,同時(shí)盡可能降低甚至避免放射線對(duì)靶區(qū)附近健康組織和器官的傷害。由于調(diào)強(qiáng)放射治療方式的射野復(fù)雜度和劑量率比其他放射治療方式的射野復(fù)雜度和劑量率高,治療實(shí)施的各個(gè)環(huán)節(jié)均有可能產(chǎn)生誤差,因此需要對(duì)調(diào)強(qiáng)放射治療計(jì)劃進(jìn)行劑量驗(yàn)證。基于a-Si EPID的劑量驗(yàn)證是目前具有良好發(fā)展前途的劑量驗(yàn)證技術(shù)之一。本論文的工作就是應(yīng)用計(jì)算機(jī)技術(shù),研究a-Si EPID子野的劑量響應(yīng)特性,探討a-Si EPID灰度影像劑量標(biāo)定的方法,為劑量驗(yàn)證等后續(xù)工作提供技術(shù)支撐。本論文首先采集了數(shù)字醫(yī)用直線加速器在不同輸出劑量情況下a-Si EPID裝置的灰度影像,并同時(shí)采用三維水箱采集了照射野輸出劑量的空間分布數(shù)據(jù)。由于a-Si EPID灰度影像的空間分辨率遠(yuǎn)高于采集到的劑量數(shù)據(jù)的分辨率,為了獲得充足的數(shù)據(jù),需要對(duì)采集的文本格式的低分辨率劑量分布數(shù)據(jù)進(jìn)行數(shù)據(jù)結(jié)構(gòu)轉(zhuǎn)換和數(shù)據(jù)補(bǔ)充。數(shù)據(jù)采集過(guò)程中,往往有很多因素會(huì)影響數(shù)據(jù)采集的精確度,而神經(jīng)網(wǎng)絡(luò)具有較強(qiáng)的自學(xué)習(xí)能力、處理不精確信息的能力、較強(qiáng)的抗干擾和抗噪聲能力,本文采用神經(jīng)網(wǎng)絡(luò)來(lái)處理放射治療學(xué)的數(shù)據(jù)。在研究分析各種插值、擬合算法特點(diǎn)的基礎(chǔ)上,根據(jù)照射野劑量分布的特點(diǎn),采用三次樣條插值法對(duì)劑量平穩(wěn)區(qū)域進(jìn)行插值,采用基于遺傳算法的廣義回歸神經(jīng)網(wǎng)絡(luò)模型對(duì)劑量分布復(fù)雜的區(qū)域進(jìn)行擬合,最終形成2048×2048的二維矩陣,并采用BP神經(jīng)網(wǎng)絡(luò)模型研究非晶硅EPID子野的劑量響應(yīng)特性。對(duì)1-12MU影像按射線劑量的大小順序進(jìn)行融合,鑒于非晶硅EPID子野劑量響應(yīng)特性的分散性和1-12MU融合影像的所具有的空間特征,設(shè)計(jì)并建立空間線性神經(jīng)網(wǎng)絡(luò)模型,使用驗(yàn)證樣本評(píng)價(jià)該模型,表明采用該模型來(lái)實(shí)現(xiàn)劑量標(biāo)定的可行性。
[Abstract]:With the continuous improvement of physics and biological science theory and the rapid development of radiotherapy technology, radiotherapy has become one of the main methods of tumor treatment. In order to improve the therapeutic effect and reduce the side effects of radiation on the human body, it is necessary to accurately control the dose distribution of the radiation field during the radiotherapy of patients. At the same time minimize or even avoid radiation damage to healthy tissues and organs near the target area. Because the radiation field complexity and dose rate of intensity modulated radiation therapy are higher than those of other radiation therapy methods, errors may occur in all aspects of the treatment. It is therefore necessary to verify the dose of the IMRT program. Dose verification based on a-Si EPID is one of the promising dose verification techniques. The work of this paper is to use computer technology to study the dose response characteristics of a-Si EPID subfield, to discuss the method of dose calibration for a-Si EPID gray image, and to provide technical support for further work such as dose verification. In this paper, the grayscale images of the a-Si EPID device under different output doses of the digital medical linear accelerator are first collected, and the spatial distribution data of the output dose of the irradiation field are also collected by using the three-dimensional water tank. Because the spatial resolution of a-Si EPID gray image is much higher than that of the collected dose data, in order to obtain sufficient data, it is necessary to transform and supplement the collected low-resolution dose distribution data in text format. In the process of data acquisition, there are many factors which will affect the accuracy of data acquisition, but the neural network has strong ability of self-learning, dealing with imprecise information, strong anti-interference and anti-noise ability. Neural networks are used to process radiotherapy data. On the basis of studying and analyzing the characteristics of various interpolation and fitting algorithms, according to the characteristics of radiation field dose distribution, the cubic spline interpolation method is used to interpolate the steady region of dose. The generalized regression neural network model based on genetic algorithm was used to fit the complex region of dose distribution, and a two-dimensional matrix of 2048 脳 2048 was formed. The dose response characteristics of EPID subfield in amorphous silicon were studied by BP neural network model. The 1-12MU images are fused in the order of radiation dose. In view of the dispersion of the field dose response characteristics of amorphous silicon EPID and the spatial characteristics of 1-12MU fusion images, a spatial linear neural network model is designed and established. Validation samples are used to evaluate the model, which shows the feasibility of using the model to achieve dose calibration.
【學(xué)位授予單位】：河南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R730.55;TP183

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本文編號(hào)：1831127