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Dosimetric effect of CT contrast agent in CyberKnife treatment plans

Cited 8 time in Web of Science Cited 10 time in Scopus
Authors

Kim, Hee Jung; Chang, Ah Ram; Park, Yang-Kyun; Ye, Sung-Joon

Issue Date
2013-10-18
Publisher
BioMed Central
Citation
Radiation Oncology, 8(1):244
Keywords
CyberKnifeCT contrastSmall field beam
Abstract
Background
To investigate the effect of computed tomography (CT) contrast enhancement (CE) on the 3D dose distributions of non-coplanar small field beams in the CyberKnife (CK) treatment planning system (TPS) for the stereotactic ablative radiotherapy (SABR).

Methods
Twenty-two pre-CE CT treatment plans were recruited to this retrospective plan study. Their post-CE CT plans were based on the pre-CE CT plan data and calculated using the same MU and beam paths in either Ray-Tracing or Monte Carlo (MC) algorithms. The differences in the doses of the beam path and the reference point between the pre- and post-CE CT plans were compared. The minimum, maximum, and mean doses in dose-volume histograms (DVHs) of target and organs-at-risk (OARs) were also compared.

Results
The dose differences between the pre- and post-CE plans in a single beam path were less than 1.05% in both calculation algorithms, with respect to the prescription dose. At the center of the target volume, it was 1.9% (maximum 6.2%) in Ray-Tracing and 1.6% (maximum 4.0%) in MC. The CA effect showed on average 1.2% difference in the OAR maximum dose (maximum 7.8% in Ray-Tracing and 7.2% in MC). In the lung cases, the CT CE resulted in a dose difference of 2.4% (from 1.0% to 6.5%) without the calculation algorithm effect (maximum 20.3%).

Conclusions
The CK treatment plan using the post-CE CT generally afforded less than 2% dose differences from the pre-CE CT plan. However, it could be up to 7.8% depending on the target positions in a body and be more than 20% with the calculation algorithms. Thus, the post-CE CT in CK treatment plans should be used with careful consideration for the CA effect, target position, and calculation algorithm factors.
Language
English
URI
https://hdl.handle.net/10371/109831
DOI
https://doi.org/10.1186/1748-717X-8-244
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