Accurate dosimetry in small field proton therapy is challenging, particularly for applications such as ocular therapy, and suitable detectors for this purpose are sought. The Exradin W1 plastic scintillating fibre detector is known to out-perform most other detectors for determining relative dose factors for small megavoltage photon beams used in radiotherapy but its potential in small proton beams has been relatively unexplored in the literature. The 1 mm diameter cylindrical geometry and near water equivalence of the W1 makes it an attractive alternative to other detectors. This study examines the dosimetric performance of the W1 in a 74 MeV proton therapy beam with particular focus on detector response characteristics relevant to relative dose measurement in small fields suitable for ocular therapy. Quenching of the scintillation signal is characterized and demonstrated not to impede relative dose measurements at a fixed depth. The background cable-only (Čerenkov and radio-fluorescence) signal is 4 orders of magnitude less than the scintillation signal, greatly simplifying relative dose measurements. Comparison with other detectors and Monte Carlo simulations indicate that the W1 is useful for measuring relative dose factors for field sizes down to 5 mm diameter and shallow spread out Bragg peaks down to 6 mm in depth.

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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.

Inspired by the success of deep learning applications on restoration of low-dose and sparse CT images, we propose a novel method to reconstruct high-quality 4D cone-beam CT (4DCBCT) images from sparse datasets. Our approach combines the idea of bin-sharing with a deep convolutional neural network (CNN) model. More specifically, for each respiratory bin, an initial estimate of the patient sinogram is obtained by taking projections from adjacent bins and performing linear interpolation. Subsequently, the estimated sinogram is propagated through a CNN that predicts a full, high-quality sinogram. Lastly, the predicted sinogram is reconstructed with traditional CBCT algorithms such as the Feldkamp, Davis and Kress (FDK) method. The CNN model, which we referred to as the Sino-Net, was trained under different loss functions. We assessed the performance of the proposed method in terms of image quality metrics (mean square error, mean absolute error, peak signal-to-noise ratio and structural similarity) and tumor motion accuracy (tumor centroid deviation with respect to the ground truth). Overall, the presented prototype model was able to substantially improve the quality of 4DCBCT images, removing most of the streak artifacts and decreasing the noise with respect to the standard FDK reconstructions. The tumor centroid deviations with respect to the ground truth predicted by our method were approximately 0.5 mm, on average (maximum deviation was approximately 2 mm). These preliminary results are promising and encourage us to further investigate the performance of our model under more challenging imaging conditions and compare it against the state-of-the-art CBCT reconstruction algorithms.

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