ESTRO 2020 Abstract Book

S62 ESTRO 2020

residual error M , the standard deviation of the systematic error Σ , the standard deviation of the random error σ , and the required margin according to van Herk (2000) for a 5- fraction and 15-fraction schedule. Results The displacements of the CoM of the different fiducial types with respect to the tumor bed are shown in Table 1. The group mean error was not significantly different from zero. The skin marker showed the largest systematic and random errors, followed by the interstitial markers. Figure 1 shows the margins required to correct for fiducial displacement for a 5-fraction and 15-fraction schedule. The margins required were ≤ 2 mm in all directions for the surgical clips. Interstitial markers required a larger and anisotropic margin, of up to 5 mm in the left-right direction. Due to the relatively large random error, fractionation has the biggest impact on the margin for a skin marker. The average PTV volume (5 fractions) would be 113 cc for the surgical clips, 134 cc for the interstitial markers, and 169 cc for the skin marker, which is a 50% larger volume.

low-mAs fast helical CT scans (the first termed the reference image) using a 64-slice CT scanner with simultaneous abdomen-based breathing surrogate measurement. The breathing surrogate amplitude is assigned to each CT slice. Deformable image registration is used to determine the voxel-voxel motion and a breathing motion model fit to each voxel. For our system, the model uses breathing amplitude and rate as the time- dependent variables, hence is termed 5DCT. The model is subsequently used to deform the reference image to a user-selected breathing amplitude. The model residuals are used to describe overall process quality, while the original CT scans are also reconstructed to describe overall process accuracy. For the clinic, we replace the 8 phase- based CT scans with model-built scans at 8 amplitudes corresponding to 8 breathing amplitude percentiles. We evaluated the first 13 clinical patients to determine the impact and quality of the new workflow, including analyzing breathing irregularity and the model residuals. Results Seven of the 13 patients had irregular breathing, determined by examining the breathing trace during the CT scan acquisition. These seven had a combination of varying breathing amplitude and baseline drift. Two patients had wildly varying amplitudes, three had significant drift, and the last had more breath-to-breath erratic breathing. The corresponding motion model residuals were compared between regular and irregular breathing patients, with mean residual values of 1.23 mm and 1.45 mm, respectively, but the differences were not statistically significant (p = 0.27). The 95 th percentile of the residuals was used to characterize outliers, which were 2.41 mm, and 2.35 mm for the normal and abnormal breathing patterns, respectively. Figure 1 shows an example of regular (patient 2) and irregular (patient 5) breathing patterns. Table 1 shows the statistical analysis of the mean and 95 th percentile model residual errors. While the mean values of the residuals did not correlate to whether the breathing amplitude was irregular, the two patients with the largest residuals both exhibited irregular breathing patterns. Note that the irregular breathing patient shown in Figure 1 had small mean and 95 th percentile residuals.

Conclusion Based on the data taken from in-room acquired daily CT scans, surgical clips most accurately represent the position of the tumor bed. A larger margin is required if interstitial fiducials are used and a single skin marker is insufficient to accurately localize the tumor bed for daily image guidance. PH-0122 Clinical implementation of model-based CT D. Low 1 , M. Lauria 1 , B. Stiehl 1 , A. Santhanam 1 , P. Lee 2 , A. Raldow 1 , D. O'Connell 1 1 UCLA Medical Center, Department of Medical Physics, Los Angeles, USA ; 2 M.D. Anderson Cancer Center, Radiation Oncology, Houston, USA Purpose or Objective To present the first clinical implementation of a model- based CT workflow for lung cancer radiation therapy. Material and Methods This work was motivated by the lack of quantitation and the sorting-induced artifacts of commercial 4DCT. The model-based CT workflow starts with the acquisition of 25