基于图像结构转换和demons配准的无标记BEV肿瘤跟踪算法  

A markerless beam's eye view tumor tracking algorithm based on structure conversion and demons registration in medical image

作　　者：管棋 丘敏敏 黄泰茗 钟嘉健 罗宁 邓永锦[1] Guan Qi;Qiu Minmin;Huang Taiming;Zhong Jiajian;Luo Ning;Deng Yongjin(Department of Radiation Oncology,First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China)

机构地区：[1]中山大学附属第一医院放射治疗科,广州510080

出　　处：《中华放射肿瘤学杂志》2023年第4期339-346,共8页Chinese Journal of Radiation Oncology

摘　　要：目的提出可应用于图像质量差、多叶准直器(MLC)遮挡和非刚性变形的兆伏级图像的无标记射束方向观(BEV)肿瘤放疗跟踪算法。方法采用窗口模板匹配、图像结构转换和demons非刚性配准方法,解决兆伏级图像中的配准问题。在模体中生成质量保证(QA)计划并在加速器上手动设置治疗偏移后执行,收集治疗过程中的682幅电子射野影像装置(EPID)图像作为固定图像;同时采集计划系统中对应射野角度的数字重建影像(DRR)图作为浮动图像,验证算法的准确性。此外收集21例肺部肿瘤治疗患者的共533对图像进行肿瘤跟踪研究,提供治疗过程中肿瘤位置变化定量结果。图像相似度用于跟踪结果的第三方验证。结果算法可应对不同程度(10%~80%)的图像缺失,模体验证中86.8%的跟踪误差在3 mm以下,80%在2 mm以下。配准前后归一化互信息(NMI)变化为(1.182±0.026)~(1.202±0.027)(P<0.005),豪斯多夫距离(HD)变化为(57.767±6.474)~(56.664±6.733)(P<0.005)。病例结果以平移为主(-6.0~6.2 mm),但非刚性形变仍存在。配准前后NMI变化为(1.216±0.031)~(1.225±0.031)(P<0.005),HD变化为(46.384±7.698)~(45.691±8.089)(P<0.005)。结论本文算法可应对不同程度图像缺失,且在数据缺失图像的非刚性配准中表现较好,适用于不同放疗技术,为多模态、部分数据及图像质量较差的兆伏级图像处理提供了参考思路。Objective To propose a markerless beam's eye view(BEV)tumor tracking algorithm,which can be applied to megavolt(MV)images with poor image quality,multi-leaf collimator(MLC)occlusion and non-rigid deformation.Methods Window template matching,image structure transformation and demons non-rigid registration method were used to solve the registration problem in MV images.The quality assurance(QA)plan was generated in the phantom and executed after manually setting the treatment offset on the accelerator,and 682 electronic portal imaging device(EPID)images in the treatment process were collected as fixed images.Meanwhile,the digitally reconstructured radiograph(DRR)images corresponding to the field angle in the planning system were collected as floating images to verify the accuracy of the algorithm.In addition,a total of 533 images were collected from 21 cases of lung tumor treatment data for tumor tracking study,providing quantitative results of tumor location changes during treatment.Image similarity was used for third-party verification of tracking results.Results The algorithm could cope with different degrees(10%-80%)of image missing.In the phantom verification,86.8%of the tracking errors were less than 3 mm,and 80%were less than 2 mm.Normalized mutual information(NMI)varied from 1.182±0.026 to 1.202±0.027(P<0.005)before and after registration and the change of Hausdorff distance(HD)was from 57.767±6.474 to 56.664±6.733(P<0.005).The case results were predominantly translational(-6.0 mm to 6.2 mm),but non-rigid deformation still existed.NMI varied from 1.216±0.031 to 1.225±0.031(P<0.005)before and after registration and the change of HD was from 46.384±7.698 to 45.691±8.089(P<0.005).Conclusions The proposed algorithm can cope with different degrees of image missing and performs well in non-rigid registration with data missing images which can be applied in different radiotherapy technologies.It provides a reference idea for processing MV images with multi-modality,partial data and poor image quality.

关 键 词：无标记肿瘤跟踪 电子射野影像装置 Arimoto DEMONS 多叶准直器遮挡 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R730.55[自动化与计算机技术—计算机科学与技术]