Kavanaugh, James | Search NASA

DOE OSTI2015

Daily QA of linear accelerators using only EPID and OBI

Purpose: As treatment delivery becomes more complex, there is a pressing need for robust quality assurance (QA) tools to improve efficiency and comprehensiveness while simultaneously maintaining high accuracy and sensitivity. This work aims to present the hardware and software tools developed for comprehensive QA of linear accelerator (LINAC) using only electronic portal imaging devices (EPIDs) and kV flat panel detectors. Methods: A daily QA phantom, which includes two orthogonally positioned phantoms for QA of MV-beams and kV onboard imaging (OBI) is suspended from the gantry accessory holder to test both geometric and dosimetric components of a LINAC and an OBI. The MV component consists of a 0.5 cm water-equivalent plastic sheet incorporating 11 circular steel plugs for transmission measurements through multiple thicknesses and one resolution plug for MV-image quality testing. The kV-phantom consists of a Leeds phantom (TOR-18 FG phantom supplied by Varian) for testing low and high contrast resolutions. In the developed process, the existing LINAC tools were used to automate daily acquisition of MV and kV images and software tools were developed for simultaneous analysis of these images. A method was developed to derive and evaluate traditional QA parameters from these images [output, flatness, symmetry, uniformity, TPR{sub 20/10}, and positional accuracy of the jaws and multileaf collimators (MLCs)]. The EPID-based daily QA tools were validated by performing measurements on a detuned 6 MV beam to test its effectiveness in detecting errors in output, symmetry, energy, and MLC positions. The developed QA process was clinically commissioned, implemented, and evaluated on a Varian TrueBeam LINAC (Varian Medical System, Palo Alto, CA) over a period of three months. Results: Machine output constancy measured with an EPID (as compared against a calibrated ion-chamber) is shown to be within ±0.5%. Beam symmetry and flatness deviations measured using an EPID and a 2D ion-chamber array agree within ±0.5% and ±1.2% for crossline and inline profiles, respectively. MLC position errors of 0.5 mm can be detected using a picket fence test. The field size and phantom positioning accuracy can be determined within 0.5 mm. The entire daily QA process takes ∼15 min to perform tests for 5 photon beams, MLC tests, and imaging checks. Conclusions: The exclusive use of EPID-based QA tools, including a QA phantom and simultaneous analysis software tools, has been demonstrated as a viable, efficient, and comprehensive process for daily evaluation of LINAC performance.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets were separately employed to test the effectiveness of the proposed contouring error detection strategy. Results: An evaluation tool was implemented to illustrate how the proposed strategy automatically detects the radiation therapy contouring errors for a given patient and provides 3D graphical visualization of error detection results as well. The contouring error detection results were achieved with an average sensitivity of 0.954/0.906 and an average specificity of 0.901/0.909 on the centroid/volume related contouring errors of all the tested samples. As for the detection results on structural shape related contouring errors, an average sensitivity of 0.816 and an average specificity of 0.94 on all the tested samples were obtained. The promising results indicated the feasibility of the proposed strategy for the detection of contouring errors with low false detection rate. Conclusions: The proposed strategy can reliably identify contouring errors based upon inter- and intrastructural constraints derived from clinically approved contours. It holds great potential for improving the radiation therapy workflow. ROC and box plot analyses allow for analytically tuning of the system parameters to satisfy clinical requirements. Future work will focus on the improvement of strategy reliability by utilizing more training sets and additional geometric attribute constraints.

60 APPLIED LIFE SCIENCES↗