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Gross Patient Error Detection Via Cine Transmission Dosimetry

N Dang1*, V Leandro Alves1, M Ahmed2, J Siebers1, (1) University of Virginia, Charlottesville, VA, (2) Vanderbilt University Medical Center, Nashville, TN

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: To quantify the effectiveness of EPID-based cine transmission dosimetry to detect gross patient anatomic errors.

Methods: EPID image frames resulting from fluence transmitted through a patient’s anatomy 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 offset by 1-3 mm shift and 1-3 degree rotations were used to quantify expected variation due to intolerance 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 9-12 different treatment day CT’s to quantify expected inter-treatment frame variation. ROC analysis of per-frame error discrimination based upon (i) frame image differences, (ii) frame histogram comparisons, and (iii) image feature matching was used to quantify error detectability.

Results: Each error detection method was able to distinguish gross patient miss alignment and gross rotations from in-tolerance levels for the head-and-neck data set. For the prostate datasets, the methods utilized were unable to distinguish the gross errors from baseline. The feature matching algorithm is the best method based on AUC.

Conclusion: In-field gross error detection was possible for head-and-neck patient miss alignments, but not for prostate patients; presumably due to the image detail. For prostate cases, the methods used were unable to distinguish different patients.

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Funding Support, Disclosures, and Conflict of Interest: This work is supported by Varian Medical Systems.

Keywords

Electronic Portal Imaging, Radiation Therapy, ROC Analysis

Taxonomy

Not Applicable / None Entered.

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