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Poster — Thur Eve — 51: Three‐dimensional in‐vivo EPID dosimetry of IMRT and VMAT treatments
Author(s) -
Van Uytven E,
Van Beek T,
Chytyk K,
McCurdy BMC
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4740159
Subject(s) - imaging phantom , dosimetry , nuclear medicine , voxel , image guided radiation therapy , radiation treatment planning , medical imaging , radiation therapy , computer science , medical physics , medicine , patient data , radiology , database
As radiation treatment delivery becomes more complex, including dynamic IMRT and VMAT, the argument for routine patient dose verification becomes more compelling. This work demonstrates a technique that utilizes our pre‐existing portal dose image prediction algorithm to compute 3D patient dose from recorded on‐treatment portal images. This approach can be applied on CT simulation data or daily cone‐beam CT data sets. Here we demonstrate the robustness of our dose reconstruction technique with phantom and patient examples, with delivery schemes including IMRT and VMAT. For an example prostate treatment site, 3D dose distributions reconstructed in the patient model are computed for each fraction, and DVHs presented. Results indicate that the patient dose reconstruction algorithm compares well with treatment planning system computed doses for controlled test situations. For patient examples the 3D chi comparison values (similar to the gamma comparison) ranged from 94.5% to 100% agreement for voxels > 10% maximum dose for all treatments and phantom cases. We show an example where the DVH for fraction nine of a prostate treatment fails acceptability criteria, due to a previously unnoticed positioning error. Future work involves building our patient dose reconstruction into a QA package, subsequently integrating it into a clinical workflow. We are also investigating the use of this tool as a backbone for an in‐house adaptive radiotherapy implementation. This work is supported by Varian Medical Systems.