首页> 外文期刊>Journal of applied clinical medical physics / >Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
【24h】

Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking

机译:相关模型的基线校正,可提高基于红外标记的动态肿瘤跟踪的预测准确性

获取原文
           

摘要

We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker-based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. To perform IR Tracking, a four-dimensional (4D) model was constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the model was expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, F 1 st ( x , v ) , was adjusted by the baseline drift of IR markers ( BD IR ) along the x-axis, as function F ′ ( x , v ) . Next, BD detect , that defined as the difference between the target positions indicated by the implanted fiducial markers ( P detect ) and the predicted target positions with F ′ ( x , v ) ( P predict ) was determined using orthogonal kV X-ray images at the peaks of the P detect of the end-inhale and end-exhale phases for 10 s just before irradiation. F ′ ( x , v ) was corrected with BD detect to compensate for the residual error. The final corrected 4D model was expressed as F cor ( x , v ) = F 1 st { ( x ? BD IR ) , v } ? BD detect . We retrospectively applied this function to 53 paired log files of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between P detect and P predict ( | E p | ) was compared between F 1 st ( x , v ) and F cor ( x , v ) . The median 95th percentile of | E p | (units: mm) was 1.0, 1.7, and 3.5 for F 1 st ( x , v ) , and 0.6, 1.1, and 2.1 for F cor ( x , v ) in the left–right, anterior–posterior, and superior–inferior directions, respectively. Over all treatment sessions, the 95th percentile of | E p | peaked at 3.2 mm using F cor ( x , v ) compared with 8.4 mm using F 1 st ( x , v ) . Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation.PACS number: 87.19.rs, 87.19.Wx, 87.56.-v, 87.59.-e, 88.10.gc
机译:我们先前发现,使用Vero4DRT系统,外部和内部呼吸运动的基线漂移会降低基于红外(IR)标记的动态肿瘤跟踪辐射(IR Tracking)的预测准确性。在这里,我们提出了一种基线校正方法,该方法可在光束传输之前立即应用,以提高红外跟踪的预测精度。为了进行IR跟踪,在治疗开始时构建了一个四维(4D)模型以关联内部和外部呼吸信号,并使用涉及IR标记位置(x)及其速度( v),即函数F(x,v)。首先,通过IR标记(BD IR)沿x轴的基线漂移,将第一个4D模型F 1st(x,v)调整为函数F'(x,v)。接下来,使用正交kV X射线图像确定BD检测,该检测定义为植入的基准标记指示的目标位置(P检测)与F'(x,v)的预测目标位置之间的差异(P预测)在辐照前的10 s内,在吸气末期和呼气末期的P峰检测P。用BD检测校正F′(x,v)以补偿残余误差。最终校正后的4D模型表示为F cor(x,v)= F 1 st {(x?BD IR),v}? BD检测。我们回顾性地将此功能应用于12位接受IR追踪的肺癌患者的4D模型的53个配对日志文件。在F 1 st(x,v)和F cor(x,v)之间比较了P检测和P预测(| E p |)之间的绝对差的95%。的中位数95%| p F 1 st(x,v)的单位(毫米)分别为1.0、1.7和3.5,F左右(前,后和上)的F cor(x,v)分别为0.6、1.1和2.1。下方向。在所有治疗期间,| p使用F cor(x,v)的峰值为3.2 mm,而使用F 1 st(x,v)的峰值为8.4 mm。我们提出的方法通过在辐照前立即校正基线漂移来提高红外跟踪的预测精度.PACS编号:87.19.rs,87.19.Wx,87.56.-v,87.59.-e,88.10.gc

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号