Abstract Book

ESTRO 37

S535

PO-0971 Monte Carlo validation of a new dual-energy CT method for proton therapy in a patient-like geometry A. Lalonde 1 , C. Remy 2 , E. Baer 3 , H. Bouchard 2 1 Centre Hospitalier de l'Université de Montréal, Radio- Oncologie, Montréal, Canada 2 Universite de Montreal, Physique, Montreal, Canada 3 University College London, Medical Physics and Biomedical Engineering, London, United Kingdom Purpose or Objective To evaluate the performance of a novel method to extract tissue parameters for Monte Carlo (MC) dose calculation using dual-energy CT (DECT) in the context of proton therapy treatment planning. Material and Methods A novel and highly realistic ground truth numerical phantom is created using a real patient’s pelvis scan. Contours made by an expert are used to define tissues and assign known chemical compositions of human reference tissues. The density of each voxel is overwritten following the original distribution of CT numbers. From this numerical phantom, simulated images are created for single-energy CT (SECT) and DECT with different levels of random noise. The conversion from simulated CT data to MC inputs is done using the method of Schneider et al. (2000) for SECT, and a recently published method called eigentissue decomposition for DECT. Dose distributions for single proton beams are calculated using the MC code TOPAS with SECT and DECT determined inputs, and compared to ground truth dose distributions in order to quantify the error on range associated for both approaches. Results For noiseless images, the DECT-based tissue segmentation outperforms the SECT approach in the context of MC treatment planning with a range error of almost 0 mm, compared to 1.5 mm for the SECT approach (figure 1), as previously predicted by several studies. In the presence of a realistic level of random noise, errors on the range of 0.5 mm and 1.8 mm are obtained for DECT and SECT respectively (figure 2), indicating that DECT used with the eigentissue decomposition predicts proton ranges more accurately than SECT for MC dose calculation, even in the presence of noise. In terms of dose distribution, the root-mean squared error on the absorbed dose on the whole patient volume for noiseless images is 0.17% and 0.05% for SECT and DECT respectively. With a realistic level of noise, these values grow to 1.22% and 0.8%, validating the improved reliability provided by DECT in the context of MC dose calculation.

clinicians on the CT images (512 x 512 pixels, pixel size 0.9766 ). Two quantisation methods were studied: Group Uniform Quantiser (GUQ) and Individualised Uniform Quantiser (IUQ). GUQ has an intensity range optimised for the whole patient group whereas IUQ quantises an intensity range optimised for each individual patient. Both methods were applied to each patient dataset in levels from 4 to 256 to understand the impact on discriminating powers of entropy calculated from 3D GLCM, one of the most robust features reported. Results Figure (1a, b) plots entropy vs quantisation levels for all cases using GUQ and IUQ. Intersections in the entropy lines are present in both methods, which emphasises the instability and sensitivity of entropy to choice of quantisation levels. Entropy was ranked from lowest to highest for all cases in all quantisation levels. The ranks do not correspond with GTV size, indicating entropy is not a function of tumour size. Cases were grouped by entropy into Group Low (GL - lowest 19) and Group High (GH – highest 18). For a given number of bins, only 2/3 cases stay in the same group (GH or GL) when one quantisation method is compared with the other. With GUQ, 10 cases remained in GL whereas 7 remained in GL with IUQ (3 remain unchanged with both methods). 9 cases remained in GH for GUQ and 10 cases for IUQ (6 cases unchanged for both methods) for all quantisation levels. Table 1 shows large changes from 8 to 16 bins and 32 to 64 bins for both methods, stability increased with GUQ from 64 bins but less so for IUQ. Therefore using quantisation levels from one single patient scan may not produce a robust result for grouping studies.

Figure 1: Entropy for All Cases with GUQ and IUQ

Figure 1. a) Dose distribution calculated with TOPAS on the ground truth numerical phantom. Relative error on absorbed dose using b) SECT tissue segmentation and c) DECT without noise.

Table 1: Patients Changed Group when n Changes by 1 Conclusion Entropy calculated from GLCM is extremely sensitive to quantisation levels and method, but stable to changes in size of the tumour volume. We recommend using GUQ with at least 64 bins to give robust results for inter- cohort comparison. Acknowledgement: Alliance Medical Limited for funding H Wang.