ESTRO 36 Abstract Book

S879 ESTRO 36 2017 _______________________________________________________________________________________________

This can potentially minimize operator dependence or even remove the need of the skilled operator. Material and Methods In this study, co-registered CT and 3D transperineal US (TPUS) volumes with corresponding anatomical structure delineations of a prostate cancer patient were available. After preprocessing, a thresholding-based segmentation algorithm was used to extract the bones from the CT scan (Fig. 1A). The retrieved bone mask and the internal perineum boundaries enabled the identification of the patients’ perineal area on the skin (Fig. 1A-B-C). Subsequently, the scrotum was localized in order to identify the underlying perineal skin. Finally, the areas on which the US probe could not be positioned in clinical practice (e.g. the region around the anus) were removed. In this way, a skin area accessible for TPUS volume acquisition was automatically identified (Fig. 1D). All probe setups proposed by the algorithm should allow visualization of the whole prostate and seminal vesicles, as well as the adjacent edges of bladder and rectum, as clinically required. To determine these setups, the accessible skin area, in combination with the structure delineations and an estimation of the potential probe pressure, was used. The setup that also allowed visualization of most of the remaining anatomical structures was considered the best option.

Conclusion The best probe setup out of 108 possible setups could potentially increase the visualization of the anatomical structures from 87% to 96%, while still fulfilling the clinical requirements. Future steps should focus on enabling skilled and unskilled workers to position the US probe according to the calculated setup. EP-1643 Simulate baseline shift uncertainties to improve robustness of proton therapy treatments K. Souris 1 , A. Barragan 1 , D. Di Perri 1 , X. Geets 1 , E. Sterpin 1 , J.A. Lee 1 1 UCL - IREC Molecular Imaging Radiology and Oncology MIRO, Molecular Imaging Radiology and Oncology MIRO, Brussels, Belgium Purpose or Objective Several studies reported both systematic and random variations of the mean position of mobile tumors from fraction to fraction. This so-called baseline shift is a major source of uncertainties for mobile targets and can jeopardize treatment quality. Unlike conventional photon therapy, the inclusion of this error in a PTV margin is inadequate in proton therapy because of the range uncertainties. Accounting for this uncertainty in a robust optimizer is much more appropriate, using for instance population-based estimations of the shifts. We developed a baseline-shift model able to automatically generate modified 4D-CT series used as uncertainty scenarios in the TPS. Material and Methods An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at planning time are generated from a 4D- CT data. The GTV contour in the MidPCT represents the mean position of the tumor along the breathing cycle. Our model can simulate a baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall, in order to allow the tumor to slide within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results After rigid registration, a baseline shift of 9.5 mm is measured between the first- and second-week 4D-CT sets (W1-CT and W2-CT). In order to validate our model, a third 4D-CT series (BS-W1-CT) was generated from W1-CT to reproduce the measured shift (Figure 1). Water equivalent thickness (WET) has been computed for each voxel of the 3 MidPCTs and revealed that the baseline shift between W1-CT and W2-CT led to a root mean square error (RMSE) of 0.52 mm in the GTV. This WET RMSE was reduced to 0.18 mm between W2-CT and the simulated BS-W1-CT. In addition, a proton therapy plan was optimized on the average W1-CT scan and recomputed on the average W2- CT and BS-W1-CT scans. Figure 2 compares the resulting DVH for all dose distributions. The dose distribution computed on BS-W1-CT reproduces the dose degradation

Results By positioning a virtual probe on the patients’ virtual skin and subsequently translating it along the Y axis (1 mm step), rotating around Y or Z axes (up to ±3 degrees with 1 degree step) or rotating around the X axis (up to ±15 degrees with 3 degree step), 12,936 possible probe setups were identified. In total 108 of these setups allowed visualization of all clinically required structures without the occurrence of blockage by the patients’ bones. In Fig. 2 the sector of the best probe setup is superimposed in yellow on the center slices of the patients’ CT volume. If the physician had been provided with this setup prior to the US scan, potentially 96% of the delineated anatomical structures could have been visualized, in comparison with 87% using the current setup (cyan sector in Fig. 2), which was manually determined by trial-and-error.