IMRT QA and gamma comparisons: The impact of detector geometry, spatial sampling, and delivery technique on gamma comparison sensitivity

Abstract

Purpose To separately quantify sensitivity differences in patient‐specific quality assurance comparisons analyzed with the gamma comparison for different measurement geometries, spatial samplings, and delivery techniques [intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT)]. Methods Error‐free calculations for 20 IMRT and 20 VMAT cases were compared to calculations with known induced errors of varying magnitudes, using gamma comparisons. Five error types (MU scaling, three different MLC errors, and collimator errors) were induced in plan calculations on three different detector geometries – ArcCHECK, MapCHECK, and Delta 4. To study detector geometry sensitivity effects alone, gamma comparisons were made with 1 mm error‐free calculations compared to 1 mm error‐induced calculations for each device. Effects of spatial sampling were studied by making the same gamma comparisons, but down‐sampling the error‐induced calculations to the real spatial sampling of each device. Additionally, 1 mm vs 1 mm comparisons between the IMRT and VMAT cases were compared to investigate sensitivity differences between IMRT and VMAT using IMRT and VMAT cohorts with similar ranges of plan complexity and average aperture size. For each case, induced error type, and device, five different gamma criteria were studied to ensure sensitivity differences between devices, spatial sampling scenarios, and delivery technique were not gamma criterion specific, resulting in over 36,000 gamma comparisons. Results For IMRT cases, Delta4 and MapCHECK devices had similar error sensitivities for lagging leaf, bank shift, and MU errors, while the ArcCHECK had considerably lower sensitivity than the planar‐type devices. For collimator errors and perturbational leaf errors the ArcCHECK had higher error sensitivity than planar‐type devices. This behavior was independent of gamma parameters (percent dose difference, distance‐to‐agreement, and low dose threshold), though use of local normalization resulted in error sensitivites that were markedly similar between all three devices. Differences between detector geometries were less pronounced for VMAT deliveries. Error sensitivity for a given gamma criterion when comparing IMRT and VMAT deliveries on the same devices showed that VMAT plans were more sensitive to some specific error types and less sensitive to others, when compared to IMRT plans. For the ArcCHECK device, the sensitivity of IMRT and VMAT cases was quite similar, whereas this was not the case for the planar‐type devices. When comparing error sensitivity between 1 mm vs 1 mm calculations and 1 mm vs the real spatial sampling for each device, results showed that increased spatial sampling did not systematically increase error sensitivity. Conclusions Noticeable differences in error sensitivity were observed for different detector geometries, but differences were dependent on induced error type, and a particular device geometry did not offer universal improvements in error sensitivity across studied error types. Thi...

Using machine learning to predict gamma passing rate in volumetric‐modulated arc therapy treatment plans

“… 21 , 22 , 23 , 24 The chance of finding these errors is low using a diode‐array detector (e.g., ArcCHECK phantom) due to low detector density. 21 , 25 , 26 …”

“…It has been indicated that machine log file analysis is a robust and efficient way to detect errors originating from human mistakes, flawed planning, and data transfer problems 21–24 . The chance of finding these errors is low using a diode‐array detector (e.g., ArcCHECK phantom) due to low detector density 21,25,26 …”

“…[21][22][23][24] The chance of finding these errors is low using a diode-array detector (e.g., ArcCHECK phantom) due to low detector density. 21,25,26 Therefore, a total of 262 and 214 trajectory log files for Edge and TrueBeam, respectively, were analyzed using Pylinac (Ver 3.0) to find any potential errors and ensure the accuracy of plan deliverability. The percentage error was calculated by using the following equation:…”

Application of a linear interpolation algorithm in radiation therapy dosimetry for 3D dose point acquisition

“…Additionally, a detector arrayʼs dosimetric characteristics and spatial resolution will also vary. For instance, p-type silicon diodes used by Delta 4 (Scandidos, Uppsala, Sweden) are spaced at 0.5 cm intervals in the central 6 × 6 cm 2 region, and 1 cm spacing outside the central area of the array 7 . The center spacing of adjacent ion chambers in the Octavius 729 detector array (PTW, Freiburg, Germany) is 1 cm, while the spacing of the Octavius 1500 detector array (PTW, Freiburg, Germany) is 0.707 cm 11 .…”

“…Generally, measured doses are compared to the TPS dose by calculating the γ pass rate and/ or reconstructing dose based on patientsʼ anatomical structures on CT images 6 . Because detector arrays such as ion chambers and semiconductors can provide the unambiguous interpretation on measurement dose and the ability to compare measurement to calculation within clinically meaningful accuracy thresholds, a variety of studies and products have been developed for extracting dose distributions on anatomical structures and verifying external beam radiation treatment plans with 2D or 3D detector arrays [7][8][9][10] . However, these data points exist only in discrete individual detectors.…”