Evaluation of a high resolution diode array for CyberKnife quality assurance.

PURPOSE: The CyberKnife quality assurance (QA) program relies mainly on the use of radiochromic film (RCF). We aimed at evaluating high-resolution arrays of detectors as an alternative to films for CyberKnife machine QA. METHODS: This study will test the SRS Mapcheck (Sun Nuclear, Melbourne, Florida, USA) diode array and its own software, which allows three tests of the CyberKnife QA program to be performed. The first one is a geometrical accuracy test based on the delivery of two orthogonal beams (Automated Quality Assurance, AQA). Besides comparing the constancy and repeatability of both methods, known errors will be introduced to check their sensitivity. The second checks the constancy of the iris collimator field sizes (Iris QA). Changes in the field sizes will be introduced to study the array sensitivity. The last test checks the correct positioning of the multileaf collimator (MLC). It will be tested introducing known systematic displacements to whole banks and to single leaves. RESULTS: The results of the RCF and diode array were equivalent (maximum differences of 0.18 ± 0.14 mm) for the AQA test, showing the array a higher reproducibility. When known errors were introduced, both methods behaved linearly with similar slopes. Regarding Iris QA, the array measurements are highly linear when changes in the field sizes are introduced. Linear regressions show slopes of 0.96-1.17 with r2 above 0.99 in all field sizes. Diode array seems to detect changes of 0.1 mm. In MLC QA, systematic errors of the whole bank of leaves were not detected by the array, while single leaf errors were detected. CONCLUSIONS: The diode array is sensitive and accurate in the AQA and Iris QA tests, which give us the possibility of substituting RCF with a diode array. QA would be performed faster than using the film procedure, obtaining reliable results. Regarding the MLC QA, the inability to detect systematic displacements make it difficult to confidently use the detector.

On the use of Bayesian statistics in the application of adaptive setup protocols in radiotherapy.

PURPOSE: To propose adaptive setup protocols using Bayesian statistics that facilitate, based on a prediction of coverage probability, making a decision on which patients should follow daily imaging prior to treatment delivery. MATERIALS AND METHODS: The suitability of the treatment margins was assessed combining interfraction variability measurements of the first days of treatment with previous data gathered from our patient population. From this information, we decided if a patient needs an online imaging protocol to perform daily isocenter correction before each treatment fraction. We applied our method to five different datasets. Protocol parameters were selected from each dataset based on coverage probability, the expected imaging workload of the treatment unit, and the accuracy of patient classification. Time trends were assessed and included in the proposed protocols. To validate the accuracy of the protocols, they were applied to a validation dataset of prostate cancer patients. RESULTS: Adaptive setup protocols lead expected population coverage >97% in all datasets analyzed when time trends were considered. The reduction in imaging workload ranged from 40% in lung treatments to 28.5% in prostate treatments. Results of the protocol on the validation dataset were very similar to those previously predicted. CONCLUSIONS: The adaptive setup protocols based on Bayesian statistics presented in this study enable the optimization of imaging workload in the treatment unit ensuring that appropriate dose coverage remains unchanged.