Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

Purpose: To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning.Methods: The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis.Results: The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and −0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five‐fold cross‐validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365.Conclusions: The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

A recent experiment of polycrystalline ${\mathrm{WB}}_2$ with hP3 (space-group 191, prototype ${\mathrm{MgB}}_2$) and hP12 (space-group 194, prototype ${\mathrm{WB}}_2$) structures was reported to realize 17-K superconductivity (SC) at 90 GPa, and the hP3 structure is believed to be responsible for this emergent SC. However, a microscopic understanding of what makes the hP3 structure so different from the hP12 structure and why the hP3 can feature such strong electron-phonon coupling (EPC) SC is still missing. Here, based on first-principles calculations, we found that in the hP3 structure, W $d$ orbitals contribute most to electronic occupation near the $E_{\mathrm{F}}$, and $d_{z^2}$ orbitals of two neighboring W atoms have some hybridization to form weak $\sigma$ bonds. The further EPC analysis indicates that the dominant $d_{z^2}$ states are strongly coupled with the out-of-plane phonon modes by stretching the $\mathrm{W}\text{−}\mathrm{W}\sigma$ bond, thereby yielding a large superconducting gap and high $T_{\mathrm{c}}$ of $\sim 35$ K. By contrast, for the hP12 structure, two neighboring W atoms are isolated without charge hybridization to form the covalent bonds, and, accordingly, their phonon modes become very stiffened, which cannot effectively couple to W $d$ orbital states associated with a lower $T_{\mathrm{c}}$ of $\sim 4$ K. Therefore, our findings not only provide an explanation for the emergent strong EPC SC in the hP3 structure, but also have important implications for the design of high-$T_{\mathrm{c}}$ superconductors among transition metal borides.