Evaluation of CBCT based dose calculation in the thorax and pelvis using two generic algorithms.

PURPOSE: This work examines the dosimetric performance of two algorithms creating a corrected CBCT (corrCBCT) and a virtual CT (vCT) implemented in a commercial treatment planning system. METHODS: 60 patients distributed across all patient groups treated with curative intent at Vejle Hospital (breast, lung, prostate and anal/rectal cancer) were selected for the present study. Clinical treatment plans were recalculated on corrCBCT and vCT, as well as a reference CT (refCT) acquired as close in time to the CBCT image as possible. Recalculated doses were compared using gamma analysis, as well as by comparing D98%, D50%, and D2% for all delineated targets and organs at risk. RESULTS: High dosimetric accuracy is demonstrated on both the corrCBCT and vCT. Gamma 2%/2mm pass rates >98% were found for all patients except two outliers still having >93% pass rates. Equivalence of all evaluated dose metrics within ±1Gy was observed for all patient groups, while the pelvic patients additionally showed equivalence for all metrics within ±1% of the refCT dose. For the thoracic patients, equivalence within ±2.5% was established for all metrics except median dose to the ipsilateral lung, calculated on corrCBCT for the breast patient group. CONCLUSION: The corrCBCT and vCT images are shown in excellent dosimetric agreement with refCT images, and show high potential for future use for streamlined adaptive radiotherapy workflows.

SU-D-218-05: Material Quantification in Spectral X-Ray Imaging: Optimization and Validation.

PURPOSE: To develop and validate a multivariate statistical method to optimize scanning parameters for material quantification in spectral x-rayimaging. METHODS: An optimization metric was constructed by extensively sampling the thickness space for the expected number of counts for m (two or three) materials. This resulted in an m-dimensional confidence region ofmaterial quantities, e.g. thicknesses. Minimization of the ellipsoidal confidence region leads to the optimization of energy bins. For the given spectrum, the minimum counts required for effective material separation can be determined by predicting the signal-to-noise ratio (SNR) of the quantification. A Monte Carlo (MC) simulation framework using BEAM was developed to validate the metric. Projection data of the m-materials was generated and material decomposition was performed for combinations of iodine, calcium and water by minimizing the z-score between the expected spectrum and binned measurements. The mean square error (MSE) and variance were calculated to measure the accuracy and precision of this approach, respectively. The minimum MSE corresponds to the optimal energy bins in the BEAM simulations. In the optimization metric, this is equivalent to the smallest confidence region. The SNR of the simulated images was also compared to the predictions from the metric. RESULTS: TheMSE was dominated by the variance for the given material combinations,which demonstrates accurate material quantifications. The BEAMsimulations revealed that the optimization of energy bins was accurate to within 1keV. The SNRs predicted by the optimization metric yielded satisfactory agreement but were expectedly higher for the BEAM simulations due to the inclusion of scattered radiation. CONCLUSIONS: The validation showed that the multivariate statistical method provides accurate material quantification, correct location of optimal energy bins and adequateprediction of image SNR. The BEAM code system is suitable for generating spectral x- ray imaging simulations.