Search

Purpose: To evaluate the accuracy of electron transport in the magnetic field of Electron Gamma Shower version 5 (EGS5) by using the special Fano cavity test., Methods: To simulate electron transport in the magnetic field, the trajectory of the electron was reconstructed with a short step length to restrict fractional energy loss, and the maximum user step length (mxustep) was set at 0.01 cm or 0.001 cm. For the special Fano cavity test, three-layer slab Fano test geometry was used, and uniform and isotropic per unit mass mono-energetic electrons with 0.01, 0.1, 1.0, and 10 MeV were permitted from the central axis of geometry in 0.35 T and 1.5 T. Furthermore, the magnetic field strength was scaled based on the mass density of the material. The relative difference between the calculated dose to gap and the theoretical value was evaluated. Furthermore, the special Fano cavity test was also performed using EGSnrc with the electron-enhanced electric and magnetic field macros under the same conditions, and the results were compared with those of EGS5., Results: Deviations in 0.35 T were within 0.3% regardless of the parameter settings. In 1.5 T, stable results within 0.3% were obtained using 0.001 cm as the mxustep, except for one at 10 MeV. Further, the accuracy of EGSnrc was within 0.2%, except for 10 MeV for a 0.2-cm gap in 1.5 T., Conclusions: EGS5 with the appropriate parameter settings enable electron transport in magnetic fields similar with the accuracy of EGSnrc., (Copyright © 2021. Published by Elsevier Ltd.)

Purpose: This study aimed to develop a deep convolutional neural network (CNN)-based dose distribution conversion approach for the correction of the influence of a magnetic field for online MR-guided adaptive radiotherapy., Methods: Our model is based on DenseNet and consists of two 2D input channels and one 2D output channel. These three types of data comprise dose distributions without a magnetic field (uncorrected), electron density (ED) maps, and dose distributions with a magnetic field. These data were generated as follows: both types of dose distributions were created using 15-field IMRT in the same conditions except for the presence or absence of a magnetic field with the GPU Monte Carlo dose in Monaco version 5.4; ED maps were acquired with planning CT images using a clinical CT-to-ED table at our institution. Data for 50 prostate cancer patients were used; 30 patients were allocated for training, 10 for validation, and 10 for testing using 4-fold cross-validation based on rectum gas volume. The accuracy of the model was evaluated by comparing 2D gamma-indexes against the dose distributions in each irradiation field with a magnetic field (true)., Results: The gamma indexes in the body for CNN-corrected uncorrected dose against the true dose were 94.95% ± 4.69% and 63.19% ± 3.63%, respectively. The gamma indexes with 2%/2-mm criteria were improved by 10% in most test cases (99.36%)., Conclusions: Our results suggest that the CNN-based approach can be used to correct the dose-distribution influences with a magnetic field in prostate cancer treatment., (Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VI CBCT ) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VI CBCT - and 4D-CT-based ventilation images (VI CT ) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VI CBCT . Correlations between the VI CBCT and VI CT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VI CBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VI CBCT was greater or equal to that of the HU-based VI CBCT . In addition, the ventilation accuracy of VI CBCT increased at low gantry speeds. Thus, the image quality of VI CBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VI CBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VI CBCT imaging into the functional avoidance planning process., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Robust feature selection in radiomic analysis is often implemented using the RIDER test-retest datasets. However, the CT Protocol between the facility and test-retest datasets are different. Therefore, we investigated possibility to select robust features using thoracic four-dimensional CT (4D-CT) scans that are available from patients receiving radiation therapy. In 4D-CT datasets of 14 lung cancer patients who underwent stereotactic body radiotherapy (SBRT) and 14 test-retest datasets of non-small cell lung cancer (NSCLC), 1170 radiomic features (shape: n = 16, statistics: n = 32, texture: n = 1122) were extracted. A concordance correlation coefficient (CCC) > 0.85 was used to select robust features. We compared the robust features in various 4D-CT group with those in test-retest. The total number of robust features was a range between 846/1170 (72%) and 970/1170 (83%) in all 4D-CT groups with three breathing phases (40%-60%); however, that was a range between 44/1170 (4%) and 476/1170 (41%) in all 4D-CT groups with 10 breathing phases. In test-retest, the total number of robust features was 967/1170 (83%); thus, the number of robust features in 4D-CT was almost equal to that in test-retest by using 40-60% breathing phases. In 4D-CT, respiratory motion is a factor that greatly affects the robustness of features, thus by using only 40-60% breathing phases, excessive dimension reduction will be able to be prevented in any 4D-CT datasets, and select robust features suitable for CT protocol of your own facility., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

For the purpose of reducing radiation pneumontisis (RP), four-dimensional CT (4DCT)-based ventilation can be used to reduce functionally weighted lung dose. This study aimed to evaluate the functionally weighted dose-volume parameters and to investigate an optimal weighting method to realize effective planning optimization in thoracic stereotactic ablative radiotherapy (SABR). Forty patients treated with SABR were analyzed. Ventilation images were obtained from 4DCT using deformable registration and Hounsfield unit-based calculation. Functionally-weighted mean lung dose (fMLD) and functional lung fraction receiving at least x Gy (fVx) were calculated by two weighting methods: thresholding and linear weighting. Various ventilation thresholds (5th-95th, every 5th percentile) were tested. The predictive accuracy for CTCAE grade ≧ 2 pneumonitis was evaluated by area under the curve (AUC) of receiver operating characteristic analysis. AUC values varied from 0.459 to 0.570 in accordance with threshold and dose-volume metrics. A combination of 25th percentile threshold and fV 30 showed the best result (AUC: 0.570). AUC values with fMLD, fV 10 , fV 20 , and fV 40 were 0.541, 0.487, 0.548 and 0.563 using a 25th percentile threshold. Although conventional MLD, V 10 , V 20 , V 30 and V 40 showed lower AUC values (0.516, 0.477, 0.534, 0.552 and 0.527), the differences were not statistically significant. fV 30 with 25th percentile threshold was the best predictor of RP. Our results suggested that the appropriate weighting should be used for better treatment outcomes in thoracic SABR., (Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

We clarified the reconstructed 3D dose difference between two different commercial software programs (Mobius3D v2.0 and PerFRACTION v1.6.4). Five prostate cancer patients treated with IMRT (74 Gy/37 Fr) were studied. Log files and cine EPID images were acquired for each fraction. 3D patient dose was reconstructed using log files (Mobius3D) or log files with EPID imaging (PerFRACTION). The treatment planning dose was re-calculated on homogeneous and heterogeneous phantoms, and log files and cine EPID images were acquired. Measured doses were compared with the reconstructed point doses in the phantom. Next, we compared dosimetric metrics (mean dose for PTV, rectum, and bladder) calculated by Mobius3D and PerFRACTION for all fractions from five patients. Dose difference at isocenter between measurement and reconstructed dose for two software programs was within 3.0% in both homogeneous and heterogeneous phantoms. Moreover, the dose difference was larger using skip arc plan than that using full arc plan, especially for PerFRACTION (e.g., dose difference at isocenter for PerFRACTION: 0.34% for full arc plan vs. -4.50% for skip arc plan in patient 1). For patients, differences in dosimetric parameters were within 1% for almost all fractions. PerFRACTION had wider range of dose difference between first fraction and the other fractions than Mobius3D (e.g., maximum difference: 0.50% for Mobius3D vs. 1.85% for PerFRACTION), possibly because EPID may detect some types of MLC positioning errors such as miscalibration errors or mechanical backlash which cannot be detected by log files, or that EPID data might include image acquisition failure and image noise., (Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)