Room: ePoster Forums
Purpose: High radio-opacity of hip prosthesis materials causes artifacts in prostate region. This, together with higher MV beam attenuation, may affect treatment planning dosimetry. Several strategies exist to offset these complications, including planning CT density overrides. We evaluate the dosimetric effect of density override approaches.
Methods: 10 unilateral and 5 bilateral hip replacement patients were selected for this study. A planning CT was done using Phillips Brilliance CT scanner for each patient. Metal artifact reduction software was not used during the scan. A 2 arc VMAT plan was created using obtained CTs and Varian Eclipse TPS (version 11) VMAT optimizer and dose distribution calculated using AAA algorithm with heterogeneity corrections. Arc avoidance sectors were used to reduce the passing of the entrance beam through the prosthesis (27-35degrees on the side of the prosthesis). All plans were normalized to cover 96% of PTV volume with prescription dose. Then the dose distribution was re-computed for each treatment plan using two patient model volumes, a) water equivalent patient body and b) water equivalent body with density overrides for hip and hip replacement. Dose distributions and DVH curves were compared for PTV and critical structures for all three calculations for each patient.
Results: Critical structures DVH curves were not very sensitive to patient volume density overrides. For single prosthesis patients OAR DVH parameters were within 1.3% of prescription for water equivalent calculation and 0.8% for model calculation with hip and implant density overrides. OAR DVH parameters showed about 3% deviations for patients with bilateral prosthesis. Hot spot deviated more for model (a), as much as 6% for bilateral prosthesis patients.
Conclusion: Metal artifacts density overrides didn't change dosimetry significantly for patients with a single hip prosthesis. For bilateral prosthesis patients PTV dose homogeneity degraded considerably when recalculated in water equivalent patient model.