Room: AAPM ePoster Library
Purpose: The commonly accepted practice in RT is to image all patients at 120kVp in order to avoid potential errors with the energy dependent electron/mass density calibration curve. While this is safe practice, the disadvantage is in missing potential superior soft tissue contrast when you vary the tube voltage. A novel Direct Density™ (DD) algorithm is available to allow use of energy-independent CT# to density, simplifying clinical workflow with one calibration curve. We compared the dose calculated from DD reconstructed CT images at a variety of tube potentials to doses produced using the standard 120kVp images.
Methods: Two phantoms were created: homogenous with solid water slabs and a heterogenous thorax approximation using ICRU slabs of solid water, bone, and lung. Scans were performed using a standard reconstruction at 120 kVp and a DD reconstruction for differing kVp (70 – 140kVp) on a SOMATOM Definition Edge (Siemens GMBH, Forchheim, Germany). Two distinct CT density curves were implemented in the treatment planning system (RaystationV9) to read both standard and DD images. Average CT numbers for each ROI were recorded. Point doses were calculated and measured for 200 MU AP plans at 6, 10, and 15 MV, and dose differences were compared. Next, an IROC thorax phantom was scanned using both kernels at 120kVp, and a VMAT plan was simulated on each. DVH plots were created for assessment.
Results: In all instances, computed DD doses were nearly identical to the standard kernel dose. Point dose measurements differed by =1%. The largest difference was for the 70kVp AP plan, producing dosimetric error of around 3cGy. VMAT plans showed negligible differences.
Conclusion: With an appropriate CT density curve, DD reconstruction algorithm is as accurate as standard algorithms at dose prediction, but allows the flexibility of using variable kVp to improve image quality for certain tissues.
Funding Support, Disclosures, and Conflict of Interest: Jainil Shah and Guillaume Grousset both work for Siemens Healthineers USA