Gross patient error detection via cine transmission dosimetry

Abstract

$\textbf{Purpose:}$ To quantify the effectiveness of EPID-based cine transmission dosimetry to detect gross patient anatomic errors. $\textbf{Method and Materials:}$ EPID image frames resulting from fluence transmitted through multiple patients anatomies are simulated for 100 msec delivery intervals for hypothetical 6 MV VMAT deliveries. Frames simulated through 10 head-and-neck CTs and 19 prostate CTs with and without 1-3 mm shift and 1-3 degree rotations were used to quantify expected in-tolerance clinical setup variations. Per-frame analysis methods to determine if simulated gross errors of (a) 10-20 mm patient miss alignment offsets and (b) 15-20 degree patient rotations could be reliably distinguished from the above baseline variations. For the prostate image sets, frames simulated through the reference CT are intercompared with (c) frames through 8-13 different CT's for the same patient to quantify expected inter-treatment frame variation. ROC analysis of per-frame error discrimination based upon (i) frame image differences, (ii) frame histogram comparisons, (iii) image feature matching, and (iv) image distance were used to quantify error detectability. $\textbf{Results:}$ Each error detection method was able to distinguish gross patient miss-alignment and gross rotations from in-tolerance levels for both H&N and prostate datasets. The image distance algorithm is the best method based on AUC. $\textbf{Conclusion:}$ In-field gross error detection was possible for gross patient miss-alignments and incorrect patients. For prostate cases, the methods used were able to distinguish different patients from daily patient variations.