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Objective:Proton pencil beam (PB) dose calculation can achieve rapid dose calculation, whereas it is inaccurate due to the approximation in dealing with inhomogeneities. Monte Carlo (MC) dose calculation is recognized as the most accurate method, but it is extremely time consuming. The aim of this study was to apply deep-learning methods to improve the accuracy of PB dose calculation by learning the difference between the MC and PB dose distribution.Methods:A model which could convert the PB dose into the MC dose in lung cancer patients treated with intensity-modulated proton therapy (IMPT) was established based on the Hierarchically Densely Connected U-Net (HD U-Net) network. PB dose and CT images were used as model input to predict the MC dose for IMPT. The beam dose and CT images of 27 non-small cell lung cancer patients were preprocessed to the same angle and normalized, and then used as model input. The accuracy of the model was evaluated by comparing the mean square error and γ passing rate (1 mm/1%) results between the predicted dose and MC dose.Results:The predicted dose showed good agreement with MC dose. Using the 1 mm/1% criteria, the average γ passing rate (voxels receiving more than 10% of maximum MC dose) between the predicted and MC doses reached (92.8±3.4)% for the test patients. The average dose prediction time for test patients was (6.72±2.26) s.Conclusion:A deep-learning model that can accurately predict the MC dose based on the PB dose and CT images is successfully developed, which can be used as an efficient and practical tool to improve the accuracy of PB dose calculation for IMPT in lung cancer patients.
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Objective:The purpose of current study was to evaluate the interplay effects in intensity-modulated proton therapy (IMPT) for lung cancer and compare the results of different Iso-energy layer repainting techniques in the mitigation of interplay effects.Methods:Eight patients with lung cancer who underwent 4DCT were retrospectively selected. A robust CTV-based IMPT plan was generated for each based on commercial TPS, considering patient setup errors ±5 mm, range uncertainties ±3.5%, and CTV time structure motion in 4DCT image. Monte Carlo dose engines were used for all IMPT plans in the final dose calculation. The 4D static dose (4DSD) and 4D dynamic dose (4DDD) were calculated using a hybrid deformable algorithm and simulated proton delivery system for interplay effects. An index[ΔI(ROI, DVH)] was developed to quantitatively evaluate the interplay effects. We applied Iso-energy layer repainting techniques with different numbers of repainting (3, 4, 5, 6, 7) to the robust IMPT plans and evaluated the difference in the mitigation of interplay effects based on the ΔI(ROI, DVH) index.Results:Due to interplay effects, the mean values of target coverage, conformity and homogeneity index reduced by 13.7%, 12.7% and 24.6%, respectively. The mean values of lung V 5Gy and V 20Gy improved by 0.8%, 3.4% and 2.6%. Compared to the IMPT plans without layer repainting, Multiple iso-energy layers repainting techniques improved the mean values of CTV coverage by 4.5%, 3.8%, 3.8%, 3.6% and 5.7%, respectively. The average values of lung V 20Gy reduced by 1.5%, 1.8%, 1.7%, 1.6% and 1.9%, respectively. Conclusions:In the robust CTV-based IMPT plans, the interplay effects degraded the target dose distribution but were mitigated using iso-energy layer repainting techniques. We recommended to use the layer repainting technique according to the characteristics of the patient.
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Objective:To demonstrate the concept of planning target volume (PTV) is not suitable for intensity proton therapy (IMPT) in lung cancer, plan differences were compared based on the concept of PTV and Internal target volume (ITV), aiming to provide clinical reference.Methods:Six patients were retrospectively selected and approved by the local ethics committee. Each of the six patients received two IMPT plans based on a synchronous accelerator model, developed by SINAP team (Shanghai Institute of Applied Physics, China Academy Science University) and commercial treatment system: one with the PTV-based robust IMPT (PTV-IMPT) plan and the other with ITV-based robust IMPT (ITV-IMPT) plan. Three beams were set in all plans, and the final dose was calculated using Monte Carlo dose algorithm. The plan quality and robustness of PTV-IMPT and ITV-IMPT plans were evaluated quantitatively.Results:Compared to the PTV-IMPT plan, ITV-IMPT plan showed better target conformity index (conformability index: 0.58 vs.0.43), better homogeneity index (homogeneity index: 0.96 vs.0.92), lower V 5Gy in normal lung tissue (13.1% vs.13.5%) and maximum dose in spinal cord (8.9 Gy vs. 9.5 Gy) as well as plan monitor unit (MU: 338 vs. 401) . In addition, ITV-IMPT plan showed more robust in target coverage (0.003-0.032 vs. 0.02-0.28), and normal lung tissue was also found a bit robust in the ITV-IMPT plan ( 0.06-0.11, 0.07-0.13). Conclusions:Compared with the PTV-IMPT plan, ITV-IMPT plan has the advantages of high planning quality, well robustness and better tumor motion mitigation. Therefore, ITV concept is recommended to be applied in the IMPT plan for lung cancer.
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With the application of magnetic resonance imaging (MRI)-guided photon therapy, the concept of combining real-time MRI guidance with proton therapy, namely, MRI-guided proton therapy (MRPT), has attracted widespread attention. It is expected that MRPT can mitigate the uncertainties during the treatment of proton therapy to make full use of the physical advantages of protons. However, multiple electromagnetic interactions between proton therapy and MRI-guided systems may lead to mutual interference between the two systems. This article review the research progress on the MRPT system, aiming to provide certain reference for the design of MRPT system.