Abstract
Single-isocenter multitarget (SIMT) stereotactic-radiosurgery (SRS) has recently emerged as a powerful treatment regimen for intracranial tumors. With high specificity, SIMT SRS allows for rapid, high-dose delivery while maintaining integrity of adjacent healthy tissues and minimizing neurocognitive damage to patients. Highly robust and accurate quality assurance (QA) tests are critical to minimize off-targets and damage to surrounding healthy tissues. We have developed a novel QA phantom, named OneIso, to accurately and precisely measure off-axis accuracy, via off-axis Winston-Lutz (OAWL), to assist SIMT SRS programs. In this study, a comparison of three different quantitative numerical methods were performed for isolating and measuring the position of ball-bearings (BBs) used in the OAWL measurement. The three methods evaluated were: 1) feature extraction technique combined with manual intervention 2) a proprietary software utilizing optical image recognition (OIR) techniques, and 3) a machine learning (ML) model employing convolutional neural networks (CNNs). These methods were used to analyze OAWL datasets gathered from a OneIso phantom deployed on a Varian TrueBeam. The precision of localizing positional BBs within the OneIso QA phantom, analysis speed, and robustness were compared across the methods. Significantly, the trained ML model utilizing CNNs was found to exhibit superior precision, analysis speed, and efficiency compared to the other two methods. These results highlight the benefit in shifting from manual and OIR methods to that of ML techniques. The incorporation of CNNs in automated QA analysis can achieve improved precision, allowing for more rapid and wider adoption of SIMT SRS for treating intracranial metastases while preserving integrity of surrounding healthy tissues.