This work presents the first validation of the integration of a comprehensive fluence model with a patient and EPID radiation transport model that accounts for patient transmission, including complex factors such as patient scatter and the energy response of the a-Si detector. The portal dose image prediction model satisfies the 3% and 3 mm criteria for IMRT fields delivered to slab phantoms and could be used for patient treatment verification.
PurposeThe aim of this study is to investigate the performance and limitations of a real-time transit electronic portal imaging device (EPID) dosimetry system for error detection during dynamic intensity modulated radiation therapy (IMRT) treatment delivery. Sites studied are prostate, head and neck (HN), and rectal cancer treatments.MethodsThe system compares measured cumulative transit EPID image frames with predicted cumulative image frames in real-time during treatment using a χ comparison with 4 %, 4 mm criteria. The treatment site-specific thresholds (prostate, HN and rectum IMRT) were determined using initial data collected from 137 patients (274 measured treatment fractions) and a statistical process control methodology. These thresholds were then applied to data from 15 selected patients including 5 prostate, 5 HN, and 5 rectum IMRT treatments for system evaluation and classification of error sources.ResultsClinical demonstration of real-time transit EPID dosimetry in IMRT was presented. For error simulation, the system could detect gross errors (i.e. wrong patient, wrong plan, wrong gantry angle) immediately after EPID stabilisation; 2 seconds after the start of treatment. The average rate of error detection was 7.0 % (prostate = 5.6 %, HN= 8.7 % and rectum = 6.7 %). The detected errors were classified as either clinical in origin (e.g. patient anatomical changes), or non-clinical in origin (e.g. detection system errors). Classified errors were 3.2 % clinical and 3.9 % non-clinical.ConclusionAn EPID-based real-time error detection method for treatment verification during dynamic IMRT has been developed and tested for its performance and limitations. The system is able to detect gross errors in real-time, however improvement in system robustness is required to reduce the non-clinical sources of error detection.
Purpose:
This work presents an algorithm used to quantify intra‐fraction motion for patients treated using deep inspiration breath hold (DIBH). The algorithm quantifies the position of the chest wall in breast tangent fields using electronic portal images.
Methods:
The algorithm assumes that image profiles, taken along a direction perpendicular to the medial border of the field, follow a monotonically and smooth decreasing function. This assumption is invalid in the presence of lung and can be used to calculate chest wall position. The algorithm was validated by determining the position of the chest wall for varying field edge positions in portal images of a thoracic phantom. The algorithm was used to quantify intra‐fraction motion in cine images for 7 patients treated with DIBH.
Results:
Phantom results show that changes in the distance between chest wall and field edge were accurate within 0.1 mm on average. For a fixed field edge, the algorithm calculates the position of the chest wall with a 0.2 mm standard deviation. Intra‐fraction motion for DIBH patients was within 1 mm 91.4% of the time and within 1.5 mm 97.9% of the time. The maximum intra‐fraction motion was 3.0 mm.
Conclusions:
A physics based algorithm was developed and can be used to quantify the position of chest wall irradiated in tangent portal images with an accuracy of 0.1 mm and precision of 0.6 mm. Intra‐fraction motion for patients treated with DIBH at our clinic is less than 3 mm.
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