Comparison of novel shielded nasopharynx applicator designs for intracavitary brachytherapy.

PURPOSE: Nasopharyngeal brachytherapy is limited in part by the radiotolerance of nearby organs like the soft palate. This study explores several novel shielding designs for an intracavitary applicator to significantly reduce soft palate dose while adhering to the constraints of standard treatment procedure. METHODS: The Monte Carlo code TOPAS is used to characterize each prototype under typical high-dose-rate treatment conditions. Mucosal surface dose maps are collected to evaluate the shields on their dose reduction to the central and soft palate planning points and uniformity in their shielding profile. Practicality with respect to patient comfort and pretreatment imaging is discussed. History-by-history standard deviations are calculated for each simulation. RESULTS: A design with elliptical tubing containing bundles of tantalum wires provides the most significant attenuation with 39% and 27% dose reduction to the center and soft palate locations, respectively. Another design utilizing miniature lead spheres loaded into a constructed cavity shows 27% and 24% dose reduction to the same locations while providing more uniform shielding and several practical benefits. Both shields are designed to be completely removable for applicator insertion and pretreatment imaging. The mean and maximum standard error of relative dose measurements was 0.36 and 1.14 percentage points, respectively. CONCLUSION: Each shielding design presented in this study provides a novel approach to safely and effectively shield healthy tissue during intracavitary nasopharyngeal brachytherapy. Analysis performed using Monte Carlo suggests that the design using metal spheres most practically shields the soft palate and should be advanced to the next stages of clinical optimization.

Learning ambidextrous robot grasping policies.

Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. This paper explores "ambidextrous" robot grasping, where two or more heterogeneous grippers are used. We present Dexterity Network (Dex-Net) 4.0, a substantial extension to previous versions of Dex-Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry. We train policies for a parallel-jaw and a vacuum-based suction cup gripper on 5 million synthetic depth images, grasps, and rewards generated from heaps of three-dimensional objects. On a physical robot with two grippers, the Dex-Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour.