Purpose: Studies have evaluated the viability of using open-face masks as an immobilization technique to treat intracranial and head and neck cancers. This method offers less stress to the patient with comparable accuracy to closed-face masks. Open-face masks permit implementation of surface guided radiation therapy (SGRT) to assist in positioning and motion management. Research suggests that changes in patient facial expressions may influence the SGRT system to generate false positional corrections. This study aims to quantify these errors produced by the SGRT system due to face motion. Methods: Ten human subjects were immobilized using open-face masks. Four discrete SGRT regions of interest (ROIs) were analyzed based on anatomical features to simulate different mask openings. The largest ROI was lateral to the cheeks, superior to the eyebrows, and inferior to the mouth. The smallest ROI included only the eyes and bridge of the nose. Subjects were asked to open and close their eyes and simulate fear and annoyance and peak isocenter shifts were recorded. This was performed in both standard and SRS specific resolutions with the C-RAD Catalyst HD system. Results: All four ROIs analyzed in SRS and Standard resolutions demonstrated an average deviation of 0.3 AE 0.3 mm for eyes closed and 0.4 AE 0.4 mm shift for eyes open, and 0.3 AE 0.3 mm for eyes closed and 0.8 AE 0.9 mm shift for eyes open. The average deviation observed due to changing facial expressions was 1.4 AE 0.9 mm for SRS specific and 1.6 AE 1.6 mm for standard resolution. Conclusion: The SGRT system can generate false positional corrections for face motion and this is amplified at lower resolutions and smaller ROIs. These errors should be considered in the overall tolerances and treatment plan when using openface masks with SGRT and may warrant additional radiographic imaging.

Purpose: To create an IMRT treatment plan quantitative score using QUANTEC dose/volume parameters to assess plan quality. Methods: 132 IMRT patient plans of various treatment sites were evaluated. The optimized plan's dose volume histogram (DVH) was exported to Velocity for evaluation. The proposed scoring was based on calculating the shortest distance from the QUANTEC objective to the DVH line of each organ. Each plan was normalized against the “perfect” plan where the organs at risk (OAR) received no dose and hence the distance between the QUANTEC objective and the DVH line was maximized. The normalized scores allowed comparing the quality of plans across the treatment sites, dosimetrists, and physicians. The scores were plotted and statistically analyzed to serve as basis for future plans. Results: The score for each site was evaluated and the average percentage score resulted in the abdomen having an average percent score of 1.68 ± 1.49, 34.61 ± 35.19, 11.00 ± 15.39, 28.44 ± 25.13, 1.99 ± 5.83 for abdomen, brain, chest, head/neck, and pelvis. Differences in scores between the treatment sites are largely attributed to OAR segmentation and proximity of the OAR to the PTV. Small score differences between dosimetrist were attributed to the number of plans they have completed per site. A larger sample of treatment plans is being evaluated to investigate the effect of dosimetrist's experience on plan quality. Conclusion: This approach allows comparison of patient treatments which will help improve patient care and treatment outcomes with a quantitative score. A larger number of patient plans is being evaluated for improved statistics.

Purpose To create an open‐source visualization program that allows one to find potential cone collisions while planning intracranial stereotactic radiosurgery cases. Methods Measurements of physical components in the treatment room (gantry, cone, table, localization stereotactic radiation surgery frame, etc.) were incorporated into a script in MATLAB (MathWorks, Natick, MA) that produces three‐dimensional visualizations of the components. A localization frame, used during simulation, fully contains the patient. This frame was used to represent a safety zone for collisions. Simple geometric objects are used to approximate the simulated components. The couch is represented as boxes, the gantry head and cone are represented by cylinders, and the patient safety zone can be represented by either a box or ellipsoid. These objects are translated and rotated based upon the beam geometry and the treatment isocenter to mimic treatment. A simple graphical user interface (GUI) was made in MATLAB (compatible with GNU Octave) to allow users to pass the treatment isocenter location, the initial and terminal gantry angles, the couch angle, and the number of angular points to visualize between the initial and terminal gantry angle. Results The GUI provides a fast and simple way to discover collisions in the treatment room before the treatment plan is completed. Twenty patient arcs were used as an end‐to‐end validation of the system. Seventeen of these appeared the same in the software as in the room. Three of the arcs appeared closer in the software than in the room. This is due to the treatment couch having rounded corners, whereas the software visualizes sharp corners. Conclusions This simple GUI can be used to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, a user can save time due to replanning of cases.