IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process

2d dose measurement using a flat panel EPID

The increasing use of intensity modulated radiation therapy (IMRT) to deliver conformal radiation treatment has prompted the search for a faster and more cost effective quality assurance (QA) system. The standard technique relies on the use of film for two-dimensional dose distribution verification. Although film is considered the gold standard and is widely used for this purpose, the procedures involved are relatively lengthy, labour intensive and costly for a multiple field IMRT verification. In this study, we investigate the use of an amorphous silicon electronic portal imaging device (a-Si EPID) to complement the film. The dosimetric behaviour of the device is studied both experimentally and numerically using the EGSnrc Monte Carlo simulation routine. The intrinsic build-up of the flat panel EPID was found to be 1.1 cm of water equivalent material. The response of the flat panel EPID was found to be linear between 0 and 300 cGy. To calibrate the flat panel EPID for two dimensional dose measurements, the deconvolution method was chosen. The scatter dose kernel required for this calibration method was calculated and characterized by varying the energy, spectrum and phantom material using a 6MV pencil beam. We found that flat panel EPID scatter kernel has as much as 80% more scattering power than the water scatter kernel in the region 1 cm away from the center of a 6MV pencil beam. This confirms that a flat panel EPID behaves significantly differently from water dosimetrically and requires an accurate dose scatter kernel for calibration. A 1.0 cm wide picket fence test pattern was used to test the accuracy of the kernel. Using the deconvolution method with the calculated dose kernels, the measurements from the flat panel EPID show improved agreement with the films.Science, Faculty ofPhysics and Astronomy, Department ofGraduat

Suppression of wind-induced torsional instability using partitioned nutation dampers

The thesis aims at the development of partitioned rectangular and toroidal dampers for suppressing wind-induced instabilities in torsion of bluff bodies like bridge-decks and bundles of transmission line conductors. To begin with, energy dissipation of the dampers as affected by the system frequency and liquid height, in the presence of partitioning, is assessed. This is followed by a qualitative flow visualization study of the surface waves to provide better appreciation of the dissipation mechanism. Finally, a set of wind tunnel tests with a square prism is undertaken to determine the effectiveness of the dampers in suppressing torsional galloping instability. Results suggest that the optimum partitioning corresponds to the compartment length to width ratio of 1.2 for the rectangular damper, while for the double toroid, it represents the diameter ratios of 1.125 and 2 for the outer and inner rings, respectively. In general, for the rectangular damper, roll motion led to a higher damping compared to the pitch degree of freedom. From flow visualization, it appeared that wave breaking as well as collision of waves promote energy dissipation. During the wind tunnel tests, both rectangular and toroidal dampers proved to be quite successful in suppressing galloping instability in torsion. The information can be used to advantage in the design of bridgedecks and high voltage transmission lines, which are often susceptible to this form of instability.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat

IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process