No abstract

This work aims to characterize the novel DRX Plus 3543C detector in terms of detective quantum efficiency (DQE) using both a mobile x-ray system called Carestream DRX Revolution Nano and a traditional x-ray system (Carestream DRX Evolution). We used the commercial system DRX Revolution Nano, equipped with a new x-ray source based on CNT technology and field emission (FE) as the electron emitter (cathode). An innovative aspect of this device is its intrinsic selection of the focal spot size. We tested the system using three IEC-specified beam qualities (RQA3, 5 and 7) in terms of modulation transfer function (MTF), normalized noise power spectra (NNPS) and DQE as defined in the IEC 62220-1-1:2015. We compared the results obtained using DRX Revolution Nano and DRX Evolution with correlation and with Bland–Altman plots to study their agreement. RQA3 MTF is slightly lower than the RQA5 and 7 curves between 0.5 and 2.5 cycles mm−1. We measured MTF values of about 0.6 at 1 lp mm−1 and about 0.28 lp mm−1 at 2 lp mm−1. The NNPS curves show a decreasing trend with the energy regarding the DRX Revolution Nano. On the other hand, the DRX Evolution NNPS curve at RQA3 is greater than the one at RQA5, but the one at RQA5 is less than the one at RQA7. The DQE(0) ranged between about 0.82 (DRX Evolution at RQA3) and 0.54 (DRX Evolution at RQA7). As expected, the squared Pearson’s correlation coefficients between the two x-ray tubes were always in an optimal agreement, and Bland–Altman plots confirmed a substantial equivalence between the two physical characterizations of the wireless detector. In conclusion, we can show that the dynamic focal selection of the system equipped with CNT does not play a substantial role in image quality compared to a traditional system in terms of physical characterisation of the detector in our measurement conditions.

This study aimed to investigate if a commercial, knowledge-based tool for radiotherapy planning could be used to estimate the amount of sparing in organs at risk (OARs) in the re-planning strategy for adaptive radiotherapy (ART). Eighty head and neck (HN) VMAT Pareto plans from our institute's database were used to train a knowledge-based planning (KBP) model. An evaluation set of another 20 HN patients was randomly selected. For each patient in the evaluation set, the planning computed tomography (CT) and 2 sets of on-board cone-beam CT, corresponding to the middle and second half of the radiotherapy treatment course, were extracted. The original plan was re-calculated on a daily deformed CT (delivered DVH) and compared with the KBP DVH predictions and with the final KBP DVH after optimisation of the plan, which was performed on the same image sets. To evaluate the feasibility of this method, the range of KBP DVH uncertainties was compared with the gains obtained from re-planning. DVH differences and ROC curve analysis were used for this purpose. On average, final KBP uncertainties were smaller than the gain in re-planning. Statistical tests confirmed significant differences between the two groups. ROC analysis showed KBP performance in terms of area under the curve (AUC) values higher than 0.7, which confirmed a good accuracy in predicted values. Overall, for 48% of cases, KBP predicted a desirable outcome from re-planning, and the final dose confirmed an effective gain in 47% of cases. We have established a systematic workflow to identify effective OAR sparing in re-planning based on KBP predictions that can be implemented in an on-line, adaptive radiotherapy process.