  $\textbf{Purpose:}$ 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.
