基于OVH描述子的IMRT放疗计划检索技术研究

发布时间：2018-04-10 11:09

本文选题：调强放射治疗计划 + 相似病例检索　；参考：《南京航空航天大学》2017年硕士论文

【摘要】：调强放射治疗(Intensity-modulated radiation therapy,IMRT)作为一种精确的放射治疗技术,已经成为目前临床上广泛应用的放疗技术之一。计划设计是IMRT技术的重要环节,其目标是寻找靶区高剂量覆盖与正常组织低剂量之间的最佳折中。目前IMRT计划设计仍然是一个反复试误且其质量依赖于物理师经验的过程,由于该过程涉及的参数较多,导致计划设计耗时较严重、难以保证计划的高质量。因此,在保证计划质量的同时,如何提高IMRT计划制定效率成为目前放疗研究的热点之一。最近的研究表明,基于先验知识放疗计划设计可有效保证IMRT计划的高质量、提高放疗计划的效率。本文针对基于OVH描述子的IMRT放疗计划检索技术进行了深入的研究。首先,详细介绍了临床应用中IMRT计划设计的步骤及IMRT计划检索技术;其次,深入研究重叠体积直方图(Overlap Volume Histogram,OVH)描述子的几何关系描述能力,提出基于形态学的OVH描述子计算方法,有效准确地计算OVH描述子;然后,提出基于互补OVH描述子的改进型相似病例检索方法,该方法能够检索出形状相似度更高的病例;最后,提出基于K-均值的放疗知识库聚类方法,使同种类别中的病例具有较大的相似度,通过计算待检索病例的所属类别进行类内检索,从而提高相似病例的检索效率。为验证所提出方法的可靠性和实用性,本文利用临床鼻咽癌病例和前列腺癌病例,详细分析了基于OVH描述子的IMRT计划检索方法的检索性能和质量控制性能。实验结果表明:1)OVH描述子能够有效地描述靶区和各危及器官间的空间位置关系,且能够检索到相似度较高的病例;2)利用相似病例的计划设计参数对新病例进行再优化,在不改变靶区剂量覆盖率的情况下,降低了危及器官的剂量,得到的放疗计划可以与资深物理师经反复优化得到的放疗计划相媲美,实现了对放疗计划质量的控制;3)用相似病例指导计划设计,避免了计划优化的反复试误过程,为提高计划设计效率奠定基础。
[Abstract]:Intensity-modulated radiation therapy (IMRT), as a precise radiotherapy technique, has become one of the widely used radiotherapy techniques in clinic.Planning design is an important part of IMRT technology, whose goal is to find the best compromise between high dose coverage of target area and low dose of normal tissue.At present, the design of IMRT plan is still a process of repeated trial and error and its quality depends on the experience of the physicist. Due to the large number of parameters involved in the process, the planning design is time-consuming and difficult to guarantee the high quality of the plan.Therefore, how to improve the efficiency of IMRT planning becomes one of the hotspots in radiotherapy research.Recent studies have shown that the design of radiotherapy plan based on prior knowledge can effectively guarantee the high quality of IMRT plan and improve the efficiency of radiotherapy plan.In this paper, IMRT radiotherapy plan retrieval technology based on OVH descriptor is studied.Firstly, the steps of IMRT plan design and IMRT plan retrieval technology in clinical application are introduced in detail. Secondly, the geometric relation description ability of overlapped Volume histogram descriptor is studied in depth, and the method of calculating OVH descriptor based on morphology is proposed.Then, an improved similar case retrieval method based on complementary OVH descriptor is proposed, which can retrieve cases with higher shape similarity.In this paper, a method based on K-means is proposed to cluster the knowledge base of radiotherapy, which makes the cases in the same category have greater similarity. The retrieval efficiency of similar cases can be improved by calculating the category of the case to be retrieved.In order to verify the reliability and practicability of the proposed method, the retrieval performance and quality control performance of IMRT plan retrieval method based on OVH descriptor were analyzed in detail by using clinical cases of nasopharyngeal carcinoma and prostate cancer.The experimental results show that the 1: 1 OVH descriptor can effectively describe the spatial position relationship between the target area and the organs at risk, and can retrieve the case with high similarity.) the new case can be reoptimized by using the planning design parameters of the similar case.Without changing the dose coverage of the target area, the dose at risk to the organ was reduced, and the resulting radiotherapy plan was comparable to that obtained by repeated optimization by a senior physicist.It realizes the quality control of radiotherapy plan and uses similar cases to guide the plan design, avoids the repeated trial and error process of the plan optimization, and lays the foundation for improving the efficiency of the plan design.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R730.55;TP391.41

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