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

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