4D in vivo dose verification for real-time tumor tracking treatments using EPID dosimetry.

The use of sophisticated techniques such as gating and tracking treatments requires additional quality assurance to mitigate increased patient risks. To address this need, we have developed and validated an in vivo method of dose delivery verification for real-time aperture tracking techniques, using an electronic portal imaging device (EPID)-based, on-treatment patient dose reconstruction and a dynamic anthropomorphic phantom. Using 4DCT scan of the phantom, ten individual treatment plans were created, 1 for each of the 10 separate phases of the respiratory cycle. The 10 MLC apertures were combined into a single dynamic intensity-modulated radiation therapy (IMRT) plan that tracked the tumor motion. The tumor motion and linac delivery were synchronized using an RPM system (Varian Medical Systems) in gating mode with a custom breathing trace. On-treatment EPID frames were captured using a data-acquisition computer with a dedicated frame-grabber. Our in-house EPID-based in vivo dose reconstruction model was modified to reconstruct the 4D accumulated dose distribution for a dynamic MLC (DMLC) tracking plan using the 10-phase 4DCT dataset. Dose estimation accuracy was assessed for the DMLC tracking plan and a single-phase (50% phase) static tumor plan, represented a static field test to verify baseline accuracy. The 3%/3 mm chi-comparison between the EPID-based dose reconstruction for the static tumor delivery and the TPS dose calculation for the static plan resulted in 100% pass rate for planning target volume (PTV) voxels while the mean percentage dose difference was 0.6%. Comparing the EPID-based dose reconstruction for the DMLC tracking to the TPS calculation for the static plan gave a 3%/3 mm chi pass rate of 99.3% for PTV voxels and a mean percentage dose difference of 1.1%. While further work is required to assess the accuracy of this approach in more clinically relevant situations, we have established clinical feasibility and baseline accuracy of using the transmission EPID-based, in vivo patient dose verification for MLC-tracking treatments.

Clinical Implementation of a Model-Based In Vivo Dose Verification System for Stereotactic Body Radiation Therapy-Volumetric Modulated Arc Therapy Treatments Using the Electronic Portal Imaging Device.

PURPOSE: To report findings from an in vivo dosimetry program implemented for all stereotactic body radiation therapy patients over a 31-month period and discuss the value and challenges of utilizing in vivo electronic portal imaging device (EPID) dosimetry clinically. METHODS AND MATERIALS: From December 2013 to July 2016, 117 stereotactic body radiation therapy-volumetric modulated arc therapy patients (100 lung, 15 spine, and 2 liver) underwent 602 EPID-based in vivo dose verification events. A developed model-based dose reconstruction algorithm calculates the 3-dimensional dose distribution to the patient by back-projecting the primary fluence measured by the EPID during treatment. The EPID frame-averaging was optimized in June 2015. For each treatment, a 3%/3-mm γ comparison between our EPID-derived dose and the Eclipse AcurosXB-predicted dose to the planning target volume (PTV) and the ≥20% isodose volume were performed. Alert levels were defined as γ pass rates <85% (lung and liver) and <80% (spine). Investigations were carried out for all fractions exceeding the alert level and were classified as follows: EPID-related, algorithmic, patient setup, anatomic change, or unknown/unidentified errors. RESULTS: The percentages of fractions exceeding the alert levels were 22.6% for lung before frame-average optimization and 8.0% for lung, 20.0% for spine, and 10.0% for liver after frame-average optimization. Overall, mean (± standard deviation) planning target volume γ pass rates were 90.7% ± 9.2%, 87.0% ± 9.3%, and 91.2% ± 3.4% for the lung, spine, and liver patients, respectively. CONCLUSIONS: Results from the clinical implementation of our model-based in vivo dose verification method using on-treatment EPID images is reported. The method is demonstrated to be valuable for routine clinical use for verifying delivered dose as well as for detecting errors.

Investigation of a real-time EPID-based patient dose monitoring safety system using site-specific control limits.

PURPOSE: The 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. METHODS: The 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. RESULTS: Clinical 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. CONCLUSION: An 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.