Purpose: To investigate the response of detectors for proton dosimetry in the presence of magnetic fields. Material&Methods: Four ionization chambers, two thimble-type and two plane-parallel-type, and a diamond detector were investigated. All detectors were irradiated with homogeneous single-energy-layer fields, using 252.7 MeV proton beams. A Farmer ionization chamber was additionally irradiated in the same geometrical configuration, but with a lower nominal energy of 97.4 MeV. The beams were subjected to magnetic field strengths of 0, 0.25, 0.5, 0.75 and 1 T produced by a research dipole magnet placed at the room's isocenter. Detectors were positioned at 2 cm water-equivalent depth, with their stem perpendicular to both the magnetic field lines and the proton beam's central axis, in the direction of the Lorentz force. Normality and two sample statistical Student's t-tests were performed to assess the influence of the magnetic field on the detectors' responses. Results: For all detectors, a small but significant magnetic-field-dependent change of their response was found. Observed differences compared to the no magnetic field case ranged from +0.5% to-0.7%. The magnetic field dependence was found to be non-linear and highest between 0.25 and 0.5 T for 252.7 MeV proton beams. A different variation of the Farmer chamber response with magnetic field strength was observed for irradiations using lower energy (97.4 MeV) protons. The largest magnetic field effects were observed for plane-parallel ionization chambers. Conclusion: Small magnetic-field-dependent changes in the detector response were identified, which should be corrected for dosimetric applications.
To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the necessity of costly advanced generative adversarial network (GAN) techniques. Methods: The model was developed based on the OpenKBP challenge dataset, consisting of 200 and 40 head-and-neck patients for training and validation, respectively. The final model is a U-Net with additional ResNet blocks between up-and down convolutions. The results were obtained by training the model with AdamW with the One Cycle scheduler.The loss function is a combination of the L1 loss with a feature loss, which uses a pretrained video classifier as a feature extractor. The performance was evaluated on another 100 patients in the OpenKBP test dataset. The DVH metrics of the test data were evaluated, where D 0:1cc , and D mean were calculated for the organs at risk (OARs) and D 1% , D 95% , and D 99% were computed for the target structures. DVH metric differences between predicted and true dose are reported in percentage. Results: The model achieved 2nd and 4th place in the DVH and dose stream of the OpenKBP challenge, respectively. The dose and DVH score were 2.62 AE 1.10 and 1.52 AE 1.06, respectively. Mean dose differences for the different structures and DVH parameters were within AE1%. Conclusion: This straightforward approach produced excellent results. It incorporated One Cycle Learning, ResNet, and feature-based losses, which are common computer vision techniques.
Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50–0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.
Purpose: In the past years, many different neural network-based conversion techniques for synthesising CTs (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, e.g. for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated.Methods: 41 patients with tumours in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method (CBCT RS ) and a population-based dose calculation method (CBCT P op ) were employed for CBCT based dose calculation. The dose distributions were analysed using 3D global gamma analysis applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs at risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs.Results: The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0±0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0±0.8% and 99.1±0.8% for the CBCT RS and CBCT P op , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4±1.6% and 99.2±0.6% for CBCT RS and CBCT P op , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. Conclusion:The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed-up.
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