Abstract Book

S1144

ESTRO 37

1 Persson, E. et al . Int J Radiat Oncol Biol Phys 2017 99: 692-700 2 Paradis E , et al. Int J Radiat Oncol Biol Phys 2015;93:1154-1161. 3 Zijdenbos AP, et al. IEEE Trans Med Imaging 1994;13: 716–24. 4 van Herk, M., et al. Int J Radiat Oncol Biol Phys, 2000 47: 1121-1135. EP-2085 Simplification of relative electron density information in synthetic CT images for dose calculation J. Handrack 1,2 , M. Bangert 1,2 , C. Möhler 1,2 , T. Bostel 2,3,4 , S. Greilich 1,2 1 German Cancer Research Center DKFZ, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany 2 National Center for Radiation Research in Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, Germany 3 University of Heidelberg, Department of Radiation Oncology, Heidelberg, Germany 4 German Cancer Research Center DKFZ, Clinical Cooperation Unit Radiation Oncology, Heidelberg, Germany Purpose or Objective The interest in using magnetic resonance (MR) images for dose calculation in radiotherapy is currently growing quickly. However, MR images lack essential information on electron density (ED), which is studied to be overcome by creating so-called synthetic computed tomography (synCT) images from the MR image with a wide variety of approaches. Figure 1 shows histograms of the ED and MR signal from corresponding images. It suggests that the dominant peaks in the ED histogram from CT have a correspondence in the MR contrasts. This poses the question which degree of precision in ED information from the CT alone is required for dose planning. To potentially limit the complexity and computational effort of synthetic CT generation, we investigate the impact of simplifying the ED information in the CT on dose planning.

We compared the averaged Hounsfield units (HU) inside the external contour for both datasets, because dose calculation is basically affected by HU of each voxel. We calculated the Dice similarity coefficient (DSC) - defined as the ratio between the double of the intersection of two volumes and the sum of both volumes - of the external volume and the bone volume, obtained both by automatic segmentation in CT and in sCT using Eclipse (Varian). Finally, we registered the daily CBCT firstly with the CT and then with the sCT computing the shift between the two datasets along the 3 axes. The positioning error was separated in systematic (Σ) and random error 4 (σ). Results The averaged HU for the external volumen were CT:4 ± 171; sCT: -17 ± 178 for the first patient, and CT: 12 ± 189; sCT: -22 ± 150 for the second one. In both cases, the difference in HU implies a very small difference in relative electron density, which is more important in dose calculation (less than a 2%). The DSC for external volumen was: 0.98 for both patients, and for the bone volume was 0.81 for the patient 1 and 0.85 for the patient 2. In all cases the DSC value is much greater than 0.7, which is considered the limit for a good overlap between two volumes 3 . The systematic errors and random errors are shown in the table for the three cases. In the figure the translational displacement calculated from sCT and CBCT is represented versus the displacement calculated from CT and CBCT. The values are almost the same and, as expected, the slopes of the linear regressions of data are very close to 1.

Material and Methods Computed tomography (CT) images from six pelvic cancer patients were translated into relative electron density (ED) maps using a clinical Hounsfield lookup table (HLUT). The histograms of the ED maps were used for segmentation of the maps. Seven simplified ED maps (simED) per patient were produced with one, two or four tissue classes. Dose plans were optimized based on the original CT (orgCT) and all simEDs. Uncertainties from

Conclusion sCT can be used in an MRI only workflow radiotherapy department, not only for patient delineation, and dose calculation, but also for treatment positioning in prostate cancer treatment.