考虑标记点可见性的叶片运动轨迹算法
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摘要
背景与目的：由于人类高发肿瘤大多位于胸腹部(如肺癌),会受呼吸和胃肠蠕动等生理现象的影响,因此运动靶区的放射治疗方法是当前放疗领域的一个研究热点,目前的解决方法是4D放疗和实时跟踪放疗。本研究将两种方法有机地结合起来,在计划设计阶段确定叶片运动轨迹时,考虑标记点在将来治疗时的可见性,以便实现有计划的实时跟踪。本研究的主要研究内容为：一、在静态调强治疗模式(SMLC)下考虑标记点可见性最大化的叶片运动轨迹(即分子野序列)算法。在利用实时跟踪技术进行调强放射治疗时,我们需要在患者体内(靶区或靶区附近区域)植入标记点,以利用影像系统(如EPID)对患者靶区的运动进行实施跟踪。由于调强治疗时多叶准直器(MLC)会形成大小形状不同的照射子野,因此我们需要尽可能多的提高标记点的探测概率,即使得标记点尽可能的出现在EPID上。为了量化标记点的探测概率,本研究在国际上首次提出了标记点可见性的概念,即标记点可见时间与射野照射时间的百分比比值；二、在动态调强治疗模式(DMLC)下考虑标记点可见性最大化的叶片轨迹算法。该研究内容与“在静态调强治疗模式下考虑标记点可见性最大化的分子野序列算法”相关,只是在动态调强模式下进行叶片轨迹的优化,优化目标同样是标记点可见性的最大化。
材料与方法：我们首先建立优化算法的数学模型和该算法的程序流程图,而后采用MATLAB语言编写相应的程序以实现该优化算法。为了检验所提出的优化算法的可行性、正确性和计算效率,我们采用计算机随机生成的6个测试野(大测试野20×20、中测试野10×10、小测试野5×5各2个,含有1个或3个标记点)以及临床的15个测试野(3个前列腺癌病例,每个病例均采用5野放射治疗计划设计方案,并含有3个标记点)进行算法评估。
结果：对于静态调强,采用随机测试野及临床测试野进行的优化算法评估的结果表明：1)相比于初始的子野序列,优化的子野序列不会增加总实施强度级数目；2)相比于初始的子野序列,优化的子野序列提高了标记点可见性；对于动态调强,采用随机测试野及临床测试野进行的优化算法评估,也有类似的结果。另外,无论是子野序列优化算法,还是叶片轨迹优化算法,各测试野的程序运行时间均小于1s,说明本研究提出的优化算法的计算效率很高。
结论：本研究在国际上首次提出了标记点可见性的概念,很好的解决了实时跟踪放射治疗过程中,量化标记点探测效率的问题。在静态调强治疗模式下,提出了考虑标记点可见性的子野序列优化算法,使得优化后子野序列的标记点可见性可以达到最大值；在动态调强治疗模式下,提出了考虑标记点可见性的叶片轨迹优化算法,使得优化后叶片轨迹的标记点可见性也可以达到最大值。本研究提出的优化算法是可行的,正确的,并且计算效率也很高,可以作为今后开展实时跟踪放射治疗的理论基础,有很高的理论价值和应用前景。
Background and Objectives:Human tumors are mostly in the thorax and abdomen (such as lung cancer and prostate cancer), and so the tumor positions would be affected by the respiratory motion and the gastrointestinal peristalsis during radiotherapy (RT) treatment delivery. Recently, a numbe of investigations have been carried out to solve the target motion problem, and show that four-dimensional radiotherapy (4D RT) and real-time tracking radiotherapy are both promising methods to handle this problem. The purpose of this study was to combine these two methods, and to develop the algorithms for determining leaf trajectories with consideration of marker visibility. This study mainly focused on:(A) considering marker visibility during leaf sequencing for segmental intensity-modulated radiation therapy (SMLC-IMRT). For real-time tracking radiotherapy, fiducial markers need to be implanted into or nearby the patient's target, and then the markers will be monitored by using the imaging system mounted on LINAC (such as EPID). Due to the different apertures shaped by MLC during IMRT treatment delivery, the detection probability of fiducial marker needs to be as high as possible, that is, the visible time of markers on EPID needs to be as long as possible. In order to quantify the detection probability, this study proposed the concept of marker visibility, that is, the percentage ratio of the visible time of markers to the beam-on time;(B) considering marker visibility for dynamic IMRT (DMLC-IMRT). This research was related to the previous one, and the objective was also to maximize the marker visibility.
Materials and Methods:We firstly developed the optimization models and the algorithms'flowcharts of these studeis, and then programmed in MATLAB language. The proposed optimization algorithm was evaluated by6randomly generated test fields containing1or3markers (large size field:20×20; middle size field:10×10;small size field:5×5) and15clinical test fields containing3markers (3clinical cases of prostate cancer with5-fields treatment planning).
Results:The evaluation results of the optimal algorithm for SMLC showed that:a) the total delivered intensities were kept constant (i.e., beam-on time was not increased);b) the marker visibility was maximized after the leaf sequence optimization. The results for DMLC also showed that the marker visibility was maximized after the leaf trajectory optimization without increasing the total delivered intensities. Moreover, the computation efficiency was very high for both optimization algorithms (less than1s for every test field).
Conclusions:This work proposed the concept of marker visibility which to quantify the detection probability of fiducial markers during the real-time tracking radiotherapy treatment delivery. The marker visibility could be maximized by using the optimization algorithms for both SMLC and DMLC mode. Moreover, these algorithms were feasible and high efficient, and could be the theoretical basis of developing real-time tracking radiotherapy.
材料与方法：我们首先建立优化算法的数学模型和该算法的程序流程图,而后采用MATLAB语言编写相应的程序以实现该优化算法。为了检验所提出的优化算法的可行性、正确性和计算效率,我们采用计算机随机生成的6个测试野(大测试野20×20、中测试野10×10、小测试野5×5各2个,含有1个或3个标记点)以及临床的15个测试野(3个前列腺癌病例,每个病例均采用5野放射治疗计划设计方案,并含有3个标记点)进行算法评估。
结果：对于静态调强,采用随机测试野及临床测试野进行的优化算法评估的结果表明：1)相比于初始的子野序列,优化的子野序列不会增加总实施强度级数目；2)相比于初始的子野序列,优化的子野序列提高了标记点可见性；对于动态调强,采用随机测试野及临床测试野进行的优化算法评估,也有类似的结果。另外,无论是子野序列优化算法,还是叶片轨迹优化算法,各测试野的程序运行时间均小于1s,说明本研究提出的优化算法的计算效率很高。
结论：本研究在国际上首次提出了标记点可见性的概念,很好的解决了实时跟踪放射治疗过程中,量化标记点探测效率的问题。在静态调强治疗模式下,提出了考虑标记点可见性的子野序列优化算法,使得优化后子野序列的标记点可见性可以达到最大值；在动态调强治疗模式下,提出了考虑标记点可见性的叶片轨迹优化算法,使得优化后叶片轨迹的标记点可见性也可以达到最大值。本研究提出的优化算法是可行的,正确的,并且计算效率也很高,可以作为今后开展实时跟踪放射治疗的理论基础,有很高的理论价值和应用前景。
Background and Objectives:Human tumors are mostly in the thorax and abdomen (such as lung cancer and prostate cancer), and so the tumor positions would be affected by the respiratory motion and the gastrointestinal peristalsis during radiotherapy (RT) treatment delivery. Recently, a numbe of investigations have been carried out to solve the target motion problem, and show that four-dimensional radiotherapy (4D RT) and real-time tracking radiotherapy are both promising methods to handle this problem. The purpose of this study was to combine these two methods, and to develop the algorithms for determining leaf trajectories with consideration of marker visibility. This study mainly focused on:(A) considering marker visibility during leaf sequencing for segmental intensity-modulated radiation therapy (SMLC-IMRT). For real-time tracking radiotherapy, fiducial markers need to be implanted into or nearby the patient's target, and then the markers will be monitored by using the imaging system mounted on LINAC (such as EPID). Due to the different apertures shaped by MLC during IMRT treatment delivery, the detection probability of fiducial marker needs to be as high as possible, that is, the visible time of markers on EPID needs to be as long as possible. In order to quantify the detection probability, this study proposed the concept of marker visibility, that is, the percentage ratio of the visible time of markers to the beam-on time;(B) considering marker visibility for dynamic IMRT (DMLC-IMRT). This research was related to the previous one, and the objective was also to maximize the marker visibility.
Materials and Methods:We firstly developed the optimization models and the algorithms'flowcharts of these studeis, and then programmed in MATLAB language. The proposed optimization algorithm was evaluated by6randomly generated test fields containing1or3markers (large size field:20×20; middle size field:10×10;small size field:5×5) and15clinical test fields containing3markers (3clinical cases of prostate cancer with5-fields treatment planning).
Results:The evaluation results of the optimal algorithm for SMLC showed that:a) the total delivered intensities were kept constant (i.e., beam-on time was not increased);b) the marker visibility was maximized after the leaf sequence optimization. The results for DMLC also showed that the marker visibility was maximized after the leaf trajectory optimization without increasing the total delivered intensities. Moreover, the computation efficiency was very high for both optimization algorithms (less than1s for every test field).
Conclusions:This work proposed the concept of marker visibility which to quantify the detection probability of fiducial markers during the real-time tracking radiotherapy treatment delivery. The marker visibility could be maximized by using the optimization algorithms for both SMLC and DMLC mode. Moreover, these algorithms were feasible and high efficient, and could be the theoretical basis of developing real-time tracking radiotherapy.
引文
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