A delivery quality assurance tool based on the actual leaf open times in tomotherapy.

PURPOSE: To validate a delivery quality assurance (DQA) protocol for tomotherapy based on the measurement of the leaf open times (LOTs). In addition, to show the correlation between the mean relative LOT discrepancy and the dose deviation in the planning target volume (PTV). MATERIALS AND METHODS: We used a LOT measurement algorithm presented in a previous work on our two tomotherapy treatment units (TOMO1 and TOMO2). We generated TomoPhant plans with intentional random LOT discrepancies following Gaussian distributions of -6%, -4%, -2%, 2%, 4%, and 6%. We irradiated them on the Cheese Phantom with two ion chambers and collected the raw data on both our treatment units. Using the raw data, we measured the actual LOTs and verified that the induced discrepancies were highlightable. Then, we calculated the actual dose using Accuray's standalone dose calculator and verified that the calculated dose agreed with the ion chamber measurement. We randomly chose 60 clinical treatment plans, delivered them in air, and collected the raw detector data. We measured the actual LOTs from the raw data and calculated the corresponding dose distributions using Accuray's standalone dose calculator. We assessed the Pearson coefficient correlation of the deviation between expected and actual dose in the PTV (a) with the mean relative LOT discrepancy and (b) with the γ-index pass rate for different tolerances. RESULTS: The mean relative discrepancy between actual (measured by the algorithm) and expected LOTs on the modified TomoPhant plans was 1.10 ± 0.05% on TOMO1 and 0.02 ± 0.03% on TOMO2, respectively. The agreement between measured and calculated dose was 0.2 ± 0.3% on TOMO1 and 0.1 ± 0.3% on TOMO2, respectively. On clinical plans, the means of the relative LOT discrepancies ranged from -3.0 % to 1.4%. The dose deviation in the PTVs ranged from -1.6% to 2.4%. The Pearson coefficient correlation between the mean relative LOT discrepancy and the dose deviation in the PTV was 0.76 (P ≈ 10-15 ) on TOMO1 and 0.65 (P ≈ 10-10 ) on TOMO2, respectively. There was no correlation between the γ-index pass rate and the dose deviation in the PTV. CONCLUSION: The method made it possible to measure and to correctly highlight the LOT discrepancies on the TomoPhant plans. The dose subsequently calculated was accurate. On clinical plans, the mean LOT discrepancy correlated with the dose deviation in the PTV. This makes the mean LOT discrepancy a handy indicator of the plan quality.

In air and in vivo measurement of the leaf open time in tomotherapy using the on-board detector pulse-by-pulse data.

PURPOSE: We developed an algorithm to measure the leaf open times (LOT) from the on-board detector (OBD) pulse-by-pulse data in tomotherapy. We assessed the feasibility of measuring the LOTs in dynamic jaw mode and validated the algorithm on machine QA and clinical data. Knowledge of the actual LOTs is a basis toward calculating the delivered dose and performing efficient phantom-less delivery quality assurance (DQA) controls of the multileaf collimator (MLC). In tomotherapy, the quality of the delivered dose depends on the correct performance of the MLC, hence on the accuracy of the LOTs. MATERIALS AND METHODS: In the detector signal, the period of time during which a leaf is open corresponds to a high intensity region. The algorithm described here locally normalizes the detector signal and measures the FWHM of the high intensity regions. The Daily QA module of the TomoTherapy Quality Assurance (TQA) tool measures LOT errors. The Daily QA detector data were collected during 9 days on two tomotherapy units. The errors yielded by the method were compared to these reported by the Daily QA module. In addition, clinical data were acquired on the two units (25 plans in total), in air without attenuation material in the beam path and in vivo during a treatment fraction. The study included plans with fields of all existing sizes (1.05, 2.51, 5.05 cm). The collimator jaws were in dynamic mode (TomoEDGETM ). The feasibility of measuring the LOTs was assessed with respect to the jaw aperture. RESULTS: The mean discrepancy between LOTs measured by the algorithm and those measured by TQA was of 0 ms, with a standard deviation of 0.3 ms. The LOT measured by the method had thus an uncertainty of 1 ms with a confidence level of 99%. In 5.05 cm dynamic jaw procedures, the detector is in the beam umbra at the beginning and at the end of the delivery. In such procedures, the algorithm could not measure the LOTs at jaw apertures between 7 and maximum 12.4 mm. Otherwise, no measurement error due to the jaw movement was observed. No LOT measurement difference between air and in vivo data was observed either. CONCLUSION: The method we propose is reliable. It can equivalently measure the LOTs from data acquired in air or in vivo. It handles fully the static procedures and the 2.51 cm dynamic procedures. It handles partially the 5.05 cm dynamic procedures. The limitation was evaluated with respect to the jaw aperture.

Implementation of TomoEDGE in the independent dose calculator CheckTomo.

PURPOSE: CheckTomo is an independent dose calculation software for tomotherapy. Recently, Accuray (Accuray Inc., Sunnyvale, CA, USA) released an upgrade of its tomotherapy treatment device, called TomoEDGE Dynamic Jaws, which improves the quality of treatment plans by enhancing the dose delivery with the help of jaws motion. This study describes the upgrade of CheckTomo to that new feature. METHODS: To account for the varying width and off-axis shift of dynamic jaws fields, the calculation engine of CheckTomo multiplies the treatment field profile by a penumbral filter and shifts the dose calculation grid. Penumbral filters were obtained by dividing the edge field profiles by that of the corresponding nominal field. They were sampled at widths 1.0, 1.8, and 2.5 cm at isocenter in the edges of the 2.5 and 5 cm treatment field. RESULTS: The upgrade of CheckTomo was tested on 30 patient treatments planned with dynamic jaws. The gamma pass rate averaged over 10 abdomen plans was 95.9%, with tolerances of 3 mm/3%. For 10 head and neck plans, the mean pass rate was 95.9% for tolerances of 4 mm/4%. Finally, misplacement and overdosage errors were simulated. In each tested cases, the 2 mm/3% gamma pass rate fell below 95% when a 4 mm shift or 3% dose difference was applied. CONCLUSIONS: These results are equivalent to what CheckTomo achieves in static jaws cases. So, in terms of dose calculation accuracy and errors detection, the upgraded version of CheckTomo is as reliable for dynamic jaws plans as the former release was for static cases.