Abstract
We have developed the new dynamic moving phantom (DMP) with three axis drives to reproduce three-dimensional(3D) target motion plus one axis drive for body surface motion. The motion data for operating this phantom is able to be acquired by rehearsing dynamic tracking therapy (DTT) of a patient. In this study, the novel approach which reconstructs the 3D long time information from respiratory motion (RPM) data on the body surface during planning CT and the target motion measured by 4DCT is developed. First, the RPM data on body surface during planning CT in each patient was obtained by laser ranger (u(t)). Then, the average wave of u(t) when 4DCT are scanned was calculated and 3D target motion was obtained by analyzing 4DCT. Next, correlation functions between average wave and 3D target motion in LR, AP and SI directions were created. Finally, long time 3D target motion was generated by the correlation function with u(t) in each patient. Two different correlation models were tested. One is dual polynomial model which is composed of polynomial functions separated by inspiratory and expiratory phases (P(u(t))). Another one is simple linear fitting function (L(u(t))). RPM data of 8 lung cancer patients who participated in clinical trial with DTT using gimbaled linac were used in this study. The average absolute difference between actual target position that was already known from tracking log data and reconstructed position were calculated to evaluate two correlation models. The significance between the average absolute difference of L (u (t)) and that of P (u (t)) were evaluated by Wilcoxon’s signed rank one-sided test. Table summarizes the result of the average of absolute difference in each patient. The difference between actual target position and reconstructed position by L(u(t)) in LR, AP and SI were 0.12±0.08 mm, 0.53±0.29 mm, 0.31±0.18 mm, respectively. On the other hand, those by P(u(t)) in LR, AP and SI were 0.23±0.14 mm, 0.87±0.54 mm, 0.69±0.40 mm, respectively (p = 0.018, 0.005, 0.027). The novel approach which reconstructs the 3D target motion from RPM data during planning CT and 3D target motion measured by 4DCT were shown. Dual polynomial model that we investigated in this study was better to reconstruct 3D target motion.Abstract 3619Pt.No.Dual polynomial modelLinear modelLR (mm)SI (mm)AP (mm)LR (mm)SI (mm)AP (mm)AVSDAVSDAVSDAVSDAVSDAVSD10.070.040.300.150.140.100.090.070.300.240.400.2720.220.130.490.300.390.250.220.130.520.351.070.5930.020.010.440.200.100.080.050.020.680.360.420.2740.130.060.480.270.060.060.220.090.630.360.160.1250.060.050.690.430.480.290.340.191.860.941.440.7460.200.170.530.250.210.140.540.320.500.290.430.2470.100.060.420.350.270.140.130.091.040.780.300.1680.120.130.870.420.840.410.270.171.400.981.290.81AV0.120.080.530.290.310.180.230.140.870.540.690.40 Open table in a new tab
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