本文選題：放射治療　切入點：呼吸跟蹤　出處：《南方醫(yī)科大學(xué)》2012年碩士論文

【摘要】：放射治療是治療腫瘤最重要的手段之一,其根本目的在于使腫瘤靶區(qū)接受盡可能大劑量的照射,同時周圍正常組織接受的劑量盡可能小或者免受照射。隨著技術(shù)的發(fā)展,各種精確放療技術(shù)相繼出現(xiàn)并在臨床得到廣泛的應(yīng)用,放療患者的生存率和生活質(zhì)量得到穩(wěn)步提升。 目前,在放療過程中,仍然存在著諸多的不確定性,如患者分次間的擺位誤差,腫瘤的體積和位置隨著治療的進行發(fā)生變化,以及在治療中患者存在著各種非自主運動,尤其是呼吸運動,會導(dǎo)致胸腹部靶區(qū)發(fā)生較大的位移。呼吸運動對放療的影響貫穿著整個治療過程,包括影響計劃圖像的采集、影響放療計劃的設(shè)計和計劃的精確執(zhí)行等。目前常規(guī)的用于處理放療中呼吸運動的方法包括：運動包含法、壓迫式淺呼吸法、屏氣法和呼吸門控法等,但這些方法均存在不足,不能很好的解決呼吸運動的帶來的問題。實時跟蹤法是目前處理呼吸運動的最佳方法,通過跟蹤設(shè)備實時獲取患者的靶區(qū)位置信息,然后再將這些信息反饋給射束調(diào)整裝置,使高能射線始終對準腫瘤靶區(qū),實現(xiàn)對腫瘤的精確治療。 要實現(xiàn)實時跟蹤治療,核心問題之一就是要實現(xiàn)運動靶區(qū)準確實時的跟蹤。目前應(yīng)用最廣泛的跟蹤方法有三種：通過X線成像跟蹤植入靶區(qū)或者靶區(qū)附近的金屬標記物、通過X線成像跟蹤與靶區(qū)同步運動器官和通過光學(xué)測量裝置跟蹤患者體表標記物的運動。使用X線進行跟蹤可以準確的獲取體內(nèi)靶區(qū)的運動信息,但患者將接受大量額外劑量的照射,且植入標記物的過程是有創(chuàng)的；使用光學(xué)法能實時的獲取患者的體外運動信息,操作便利且對患者完全無創(chuàng),但由于體內(nèi)運動和體外運動之間的關(guān)系不恒定,僅通過體外跟蹤很難實現(xiàn)對體內(nèi)靶區(qū)的準確跟蹤。目前認為最可行的方法是將體內(nèi)運動測量法和體外運動測量法進行結(jié)合,充分利用二者的優(yōu)勢,實現(xiàn)對運動靶區(qū)準確實時的跟蹤。目前在臨床上有著廣泛應(yīng)用的Cyberknife治療系統(tǒng)的同步呼吸跟蹤系統(tǒng)(Synchrony系統(tǒng))就是基于這一理念。Synchrony系統(tǒng)通過一對正交的X線成像系統(tǒng)跟蹤植入體內(nèi)的金屬標記物來獲取體內(nèi)靶區(qū)的運動數(shù)據(jù)；通過紅外定位裝置獲取患者體外的運動信息；在治療中實時獲取體外運動數(shù)據(jù),并通過相關(guān)性模型推算得到靶區(qū)的位置信息；然后通過預(yù)測算法提前預(yù)知靶區(qū)的運動,最后將模型的結(jié)果傳遞給治療系統(tǒng)用于調(diào)整治療。但Synchrony系統(tǒng)同樣存在一些不足,如需通過有創(chuàng)的方法植入標記物、相關(guān)性模型和預(yù)測模型的誤差比較大等。 以Synchrony系統(tǒng)的跟蹤模型為基礎(chǔ),我們提出了一種基于體外運動信號的呼吸運動跟蹤模型,其模型架構(gòu)與Synchrony系統(tǒng)類似,但各個模塊的具體實現(xiàn)存在很大的區(qū)別。在模型中,體外運動數(shù)據(jù)通過NDI公司的POLARIS紅外定位系統(tǒng)來采集。本文以NDI提供的通信接口函數(shù)為基礎(chǔ),實現(xiàn)了一套實用的體外呼吸運動測量系統(tǒng),主要功能包括運動數(shù)據(jù)的采集、顯示和記錄,在視場的三視圖中顯示標記物的位置,實時計算呼吸運動參數(shù),通過預(yù)測算法對呼吸運動進行預(yù)測,顯示實時運動曲線、預(yù)測曲線和預(yù)測誤差等。在實驗中,將置于患者體表的紅外反射標記物的運動數(shù)據(jù)作為體外呼吸運動數(shù)據(jù)。 體內(nèi)運動數(shù)據(jù)通過數(shù)字模擬定位機進行采集,以橫膈膜頂部的運動信息作為體內(nèi)運動數(shù)據(jù)。在透視模式下,通過攝像頭記錄膈頂?shù)倪\動過程,再將視頻轉(zhuǎn)換為數(shù)字圖像,通過目標跟蹤算法自動在圖像中得到膈頂?shù)奈恢眯畔�。本文實現(xiàn)了三種目標跟蹤算法：二維最小絕對差累加和算法(MAD算法),最多鄰近點距離算法(MCD算法)和互信息算法(MI算法),結(jié)果表明,三種算法均能有效地對運動目標進行跟蹤,其中MI算法的準確性和魯棒性最好,并針對MI算法的匹配速度過慢的問題,采用了一種等步長搜索法對搜索過程進行加速。 在治療過程中,采用的運動跟蹤策略是體內(nèi)低頻采樣、體外高頻采樣,從體外運動推算體內(nèi)運動,因此要求在治療開始時建立體內(nèi)運動和體外運動的相關(guān)性模型,在治療時通過跟蹤體外運動來獲知靶區(qū)的位置信息。而且從運動跟蹤設(shè)備開始跟蹤到治療設(shè)備做好調(diào)整之間存在著系統(tǒng)延遲,包括數(shù)據(jù)獲取時間、計算處理時間、數(shù)據(jù)傳輸及機械延遲時間等,總延遲時間可以達到幾百毫秒,處理這一問題最有效的方法是通過預(yù)測模型提前預(yù)知靶區(qū)的位置信息。 對于相關(guān)性模型,將體外的運動數(shù)據(jù)作為輸入,靶區(qū)的運動估計數(shù)據(jù)作為輸出；而對于預(yù)測模型,是將當(dāng)前值作為輸入,未來值作為輸出,兩種模型的本質(zhì)很相似,故可以使用相同的函數(shù)形式,然后依照不同模型給定對應(yīng)的輸入和輸出,求出模型對應(yīng)的參數(shù),即可分別構(gòu)建出兩種模型。但是由于呼吸運動本身很不規(guī)則,同時放療對模型的準確性和實時性要求非常高,利用傳統(tǒng)的建模方法很難滿足要求,本研究提出應(yīng)用非參數(shù)回歸法構(gòu)建相關(guān)性模型和預(yù)測模型。 本文采集了11名受試者的呼吸運動數(shù)據(jù),然后分別使用非參數(shù)回歸模型、自回歸模型和BP神經(jīng)網(wǎng)絡(luò)模型進行預(yù)測,并與無預(yù)測時的結(jié)果進行比較。同時針對預(yù)測過程中出現(xiàn)的“異常狀態(tài)”,提出了一種改進的非參數(shù)回歸預(yù)測方法。最后將預(yù)測算法集成在測量系統(tǒng)中,以驗證預(yù)測算法實時測量中的有效性。經(jīng)測試表明,在不同的預(yù)測長度下,非參數(shù)回歸法能夠準確實時的對呼吸運動進行預(yù)測,改進的方法則能大幅減小呼吸運動中“異常狀態(tài)”的預(yù)測誤差。在預(yù)測長度為0.6s時,11組數(shù)據(jù)在無預(yù)測,自回歸模型、BP神經(jīng)網(wǎng)絡(luò)、非參數(shù)回歸和改進非參數(shù)回歸法的歸一化均方誤差均值分別為0.85,0.54,0.52、0.44和0.4,且與測量系統(tǒng)結(jié)合后,算法同樣能實時準確的進行預(yù)測。 Synchrony系統(tǒng)中所使用的相關(guān)性模型為混合多項式模型,其結(jié)構(gòu)簡單,但是模型的誤差較大,在實驗中,我們建立了基于非參數(shù)回歸法的相關(guān)性模型,并且與線性模型、雙二次多項式模型和神經(jīng)網(wǎng)絡(luò)模型的結(jié)果進行比較。使用7組體內(nèi)-體外同步運動數(shù)據(jù)進行了驗證,其中體內(nèi)運動數(shù)據(jù)為通過3D超聲獲取的肝臟內(nèi)血管的運動數(shù)據(jù),體外數(shù)據(jù)為通過光學(xué)測量系統(tǒng)獲得的體表標記物的運動數(shù)據(jù)。經(jīng)計算,線性、雙二次多項式、神經(jīng)網(wǎng)絡(luò)和非參數(shù)回歸四種相關(guān)性模型的歸一化均方誤差均值分別為0.35、0.32、0.30、0.19,因此基于非參數(shù)回歸的相關(guān)性模型誤差遠小于其他三種模型,并且模型構(gòu)建方便,計算的實時性好。 通過光學(xué)設(shè)備跟蹤體外運動時,可以同時跟蹤多個標記物的運動信息,采樣點越多,所包含的體外運動數(shù)據(jù)就越多,此時模型也會更復(fù)雜。在研究中,本文建立了基于非參數(shù)回歸的多體外-體內(nèi)運動相關(guān)性模型,使用1個、2個、3個體外標記物時模型的歸一化均方誤差均值分別為0.185、0.136、0.126,由此可知,模型包含的體外標記物越多,模型的誤差越小,但模型的誤差和體外標記物的組合之間并不存在確定的關(guān)系。 通過比較不同的算法建立的內(nèi)外運動相關(guān)性模型、運動預(yù)測模型和多體外-體內(nèi)相關(guān)性模型,可知,非參數(shù)回歸法在呼吸運動的建模中具有魯棒性強、準確度高、實時性好的優(yōu)點,能夠滿足實時跟蹤放療的要求。并且隨著跟蹤的進行,模型的歷史數(shù)據(jù)庫不斷的擴充,模型的準確性會不斷提高。 在文中最后對所做的工作做了小結(jié),同時說明了文中存在的一些不足并且對今后的工作做了一些展望。
[Abstract]:Radiotherapy is one of the most important means of cancer treatment, its fundamental purpose is to make the tumor target accept as large as possible dose, while the surrounding normal tissue dose as small as possible or from radiation. With the development of technology, all have the accurate radiotherapy technology and widely used in clinical, patients with radiotherapy the survival rate and life quality steadily.
At present, in the process of radiotherapy, there are still many uncertainties, such as the set-up errors were divided between the tumor size and location as the treatment of changes in the treatment of patients, and there are all kinds of non autonomous motion, especially respiratory movement, will lead to large displacement of chest and abdomen target area effect of radiotherapy on the respiratory movement. Throughout the course of treatment, including the effect of program image acquisition, influence design and planning radiotherapy precision. Methods include conventional radiotherapy for the treatment of respiratory movement in the present: exercise includes pressure method, shallow breathing, breath holding and respiratory gating, but these methods have shortcomings, solve the problems caused by respiratory motion is not very good. The real-time tracking method is the best method with respiratory motion, the target location tracking device of real-time access to patient The information is placed, and then the information is fed back to the beam adjustment device, so that the high-energy rays are always aligned with the target area of the tumor to achieve the precise treatment of the tumor.
In order to realize the real-time tracking of treatment, one of the key issues is to achieve accurate real-time tracking of moving targets. There are three kinds of currently used tracking method widely: tracking metal markers near implanted into the target area or target area by X-ray imaging, X-ray imaging tracking and target organ of synchronous motion and motion tracking in patients with skin marker the optical measuring device. The use of X-ray motion tracking information can be extracted in the target area, but a large number of patients will receive an extra dose of irradiation, the process and the implantation of markers is invasive; using optical method can obtain real-time information of patients in vitro, convenient operation and is harmless to patients. But because of the relationship between in vivo and in vitro sports movement is not constant, only by in vitro tracking is difficult to achieve accurate tracking of internal target. At present the most feasible method is to use. In the motion measurement and motion measurement method combined with in vitro, make full use of the advantages of the two, to achieve accurate real-time tracking of moving targets. The current clinical respiratory synchronization with Cyberknife treatment system is widely applied in the tracking system (Synchrony system) motion data is metal markers to track the in vivo x-ray imaging system of the the concept of.Synchrony system through a pair of orthogonal based to obtain the internal target area; external motion data is collected by infrared positioning device; in the treatment of in vitro real-time motion data, and through the relevant model to calculate position information of the target area; and then through the target prediction algorithm to predict the movement, finally the results of the model is transferred to the treatment system to adjust treatment. But the Synchrony system also has some shortcomings, such as the invasive implantation procedure, The error of the correlation model and the prediction model is very large.
The tracking model of Synchrony system as the basis, we propose a tracking model of respiratory motion external motion signal based on the model structure and similar Synchrony system, but the realization of each module are different. In the model, the in vitro motion data collected by POLARIS infrared positioning system NDI. Communication interface function this paper is based on NDI offering, to achieve a practical in vitro respiratory motion measurement system, the main functions include motion data acquisition, display and recording, marker position is displayed in the three field of view, the real-time calculation of respiratory motion parameters of respiratory motion is predicted through prediction algorithm, display real-time motion curve. The prediction curve and prediction error. In the experiment, the motion data of infrared reflective markers placed on the patient's body surface as in vitro respiratory motion data.
The body motion data collected by digital simulator, the motion information of diaphragm as body motion data. In the perspective of mode, through the camera to record the motion of the diaphragm, and then converted to digital video image, the target tracking algorithm automatically get position information of the diaphragm in the image. This paper implements three a target tracking algorithm: minimum absolute deviation algorithm (MAD algorithm), maximum close distance algorithm (MCD algorithm) and mutual information algorithm (MI algorithm), the results show that the three algorithms can track the moving target effectively, the MI algorithm the accuracy and robustness of the best, and too slow to solve the problem of matching speed of MI algorithm, using a step search method to search and accelerate the process.
In the treatment process, motion tracking strategy is applied in low frequency sampling, high frequency sampling, from in vitro calculated body movement, the correlation model therefore requires the establishment of in vivo and in vitro in motion at the start of treatment, by tracking the position information by in vitro movement to know the target area in the treatment. But from the motion tracking device tracking to make adjustments between the treatment equipment system delays, including data acquisition time, computing time, data transmission and mechanical time delay, total delay time can reach hundreds of milliseconds, processing method of this problem is the most effective location information through the prediction model to predict the target area.
The correlation model, the input data is the external motion, target motion estimation data as output; and for the prediction model, is the current value as input, the future value as output, the nature of the two models are very similar, so we can use the same function form, and then the corresponding model is given according to different input and output calculate the corresponding model parameters, respectively, to construct two models. But due to respiratory motion itself is very irregular, and the accuracy of radiotherapy for model and real-time requirements are very high, it is difficult to meet the requirements of the use of traditional modeling methods, this study proposes the application of nonparametric correlation model and regression forecasting model construction method.
This collection of respiratory motion data of 11 subjects, and then uses the non parametric regression model, regression model and BP neural network prediction model, and compared with the results without prediction. At the same time for the prediction process of the "abnormal state", proposed a modified nonparametric regression prediction methods. Finally prediction algorithm is integrated in the measurement system, the validity of the forecast algorithm in real time measurement to test. The test results show that, in the prediction of different lengths, can accurately predict the respiratory motion of non parametric regression method, the improved method can greatly reduce the respiratory motion in the abnormal state prediction the error in prediction. The length of 0.6s, 11 sets of data in prediction, regression model, BP neural network, nonparametric regression and the improved regression method the normalized mean square error of the mean was 0.85,0.54, 0.52,0.44 and 0.4, and combined with the measurement system, the algorithm can also be predicted in real time and accurately.
The correlation model of Synchrony system used in the mixed polynomial model, which has the advantages of simple structure, but the model error in the experiment, we established a correlation model based on nonparametric regression method, and compared with the linear model, two biquadratic polynomial model and neural network model. The results were validated using 7 group internal and external synchronous motion data, in which the body motion data for the motion data obtained by 3D ultrasound blood vessels in the liver, the motion data in vitro data for surface markers obtained by optical measurement system. Through calculation, linear, double two degree polynomial, four correlation models and nonparametric regression neural network were normalized the mean square error was 0.35,0.32,0.30,0.19, so the error correlation model based on nonparametric regression is much smaller than the other three models, and the model construction of convenient calculation The real time is good.
In vitro tracking movement through the optical device, motion information can simultaneously track multiple markers, more sampling points, in vitro data contains more, this model will be more complicated. In the study, this paper established the in vitro - nonparametric regression model based on the correlation of body movement, the use of 1. The 2 model, the normalized mean square error mean respectively 0.185,0.136,0.126, 3 in vitro markers when the number of marker in vitro model including the model error is small, but the combination of error and the marker in vitro model of certain relationship does not exist.
Inside and outside the motion correlation model was established by comparison of different algorithms, the motion prediction model and in vitro - in vivo correlation model, it has strong robustness in modeling the respiratory movement in non parametric regression method, high accuracy, the advantages of good real-time performance, can meet the requirements of real-time tracking and tracking with radiotherapy. The. The expansion history database model continuously, the accuracy of the model will be improved.
At the end of the paper, we make a summary of the work done, at the same time, explain some of the shortcomings in the article and make some prospects for the future work.

【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP391.41;R318.0

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