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

机译:对相关模型进行基线校正以提高基于红外标记的动态肿瘤跟踪的预测准确性

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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, F1st(x, v), was adjusted by the baseline drift of IR markers (BDIR) along the x‐axis, as function F(x, v). Next, BDdetect, that defined as the difference between the target positions indicated by the implanted fiducial markers (Pdetect) and the predicted target positions with F(x, v) (Ppredict) was determined using orthogonal kV X‐ray images at the peaks of the Pdetect of the end‐inhale and end‐exhale phases for 10 s just before irradiation. F(x, v) was corrected with BDdetect to compensate for the residual error. The final corrected 4D model was expressed as Fcor(x, v) = F1st{(x − BDIR), v}−BDdetect. 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 Pdetect and Ppredict (|Ep|) was compared between F1st(x, v) and Fcor(x, v). The median 95th percentile of |Ep| (units: mm) was 1.0, 1.7, and 3.5 for F1st(x, v), and 0.6, 1.1, and 2.1 for Fcor(x, v) in the left–right, anterior–posterior, and superior–inferior directions, respectively. Over all treatment sessions, the 95th percentile of |Ep| peaked at 3.2 mm using Fcor(x, v) compared with 8.4 mm using F1st(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:我们之前发现,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,该方法将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(BDIR)沿x轴的基线漂移来调整第一个4D模型F1st(x,v),作为函数F '(x,v)。接下来,使用正交kV确定BDdetect,即被定义为植入的基准标记(Pdetect)指示的目标位置与F '(x,v)(Ppredict)的预测目标位置之间的差值刚好在照射前,吸气和呼气阶段Pdetect的峰值X射线图像持续10 s。用BDdetect对F '(x,v)进行校正以补偿残留误差。最终校正后的4D模型表示为Fcor(x,v)= F1st {(x-BDIR),v} -BDdetect。我们将这项功能追溯应用到12位接受IR追踪的肺癌患者的4D模型的53个配对日志文件中。在F1st(x,v)和Fcor(x,v)之间比较了Pdetect和Ppredict(| Ep |)之间的绝对差的第95个百分点。 | E p |的中位数第95个百分点F 1st (x,v)的(单位:mm)为1.0、1.7和3.5,F cor (x,v)的分别为0.6、1.1和2.1分别沿左,右,前,后和上,下方向。在所有治疗期间,| E p |的第95个百分位数使用F cor (x,v)的最大峰值为3.2 mm,而使用F corst (x,v)的8.4mm峰值。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

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