An improved method for evaluating LINAC isocenter

Abstract

AbstractBackgroundThe traditional approach to medical linear accelerator (LINAC) isocenter quality assurance is to compare the radius of the LINAC isocenter, determined via analysis of Winston‐Lutz (WL) dataset, to a threshold in order to approve the LINAC for clinical use. This scalar metric provides little insight into beam‐to‐target accuracy with gantry and couch motion.PurposeTo develop a method for verifying isocenter with increased sensitivity over traditional WL techniques by accounting for geometric errors that could normally be overlooked.MethodsFrom WL images, we construct radiation beam axis and marker shift locations in a 3D coordinate system. These axes and shift positions are used to construct a new isocenter performance metric, the ‐matrix, which predicts direction and magnitude of beam‐to‐target errors across combinations of couch and gantry position. We introduce clinical isocenter as the location optimizing a cost function derived from the ‐matrix, which serves as an optimal target for tumor positioning. We demonstrated these techniques on a clinical LINAC with an initial randomly positioned marker which was subsequently repositioned based on the optimized clinical isocenter location. The marker shifts, radiation isocenter, and ‐matrix were compared before and after repositioning. We compared the new technique against typically used WL techniques using a monte carlo simulation modeling variations in LINAC geometry, marker position, and measurement noise.ResultsThe technique was successfully demonstrated on a Varian LINAC. Marker repositioning to clinical isocenter yielding an error matrix with magnitudes all below 0.81 mm. As expected, marker position had little impact on the radiation isocenter location and radius, and also had little impact on clinical isocenter location. The verification results show the accuracy of the ‐matrix to predict beam to tumor geometric inaccuracies. The Monte Carlo simulations demonstrate that the ‐matrix is more sensitive and specific for detecting potential treatment errors compared to the traditional WL techniques.ConclusionsWe have developed and demonstrated the usefulness of a framework for verifying isocenter based on a 3D model of the radiation beam axes and tumor movement from couch rotations, derived from 2D WL transmission images. The ‐matrix replaces the scalar isocenter radius as a metric of isocenter quality, providing insight into contribution of couch and radiation beams to isocenter quality, and exposing treatment errors ignored by the traditional method. The clinical isocenter provides an alternate to physical isocenter as a target for tumor positioning in cases of suboptimal LINAC geometry.