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Objective:To effectively quantify and evaluate the quality of different deformation registration algorithms, in order to enhance the possibility of implementing deformation registration in clinical practice.Methods:The Jacobian determinant mean (JDM) is proposed based on the Jacobian determinant (JD) of displacement vector field (DVF), and the Jacobian determinant error (DJDE) is introduced by incorporating the JD of the inverse DVF. The optical flow method (OF-DIR) and fast demons method with elastic regularization (FD-DIR) were tested on nasopharyngeal and lung cancer datasets. Finally, JDM and DJDE with the Jacobian determinant negative percentage (JDNP), inverse consistency error (ICE) and normalized mean square error (NMSE) were used to evaluate the registration algorithms and compare the differences evaluation indicators in different tumor images and different algorithms, and the receiver operating curve (ROC) was analyzed in evaluation.Results:In lung cancer, OF-DIR outperformed FD-DIR in terms of JDM, NMSE, DJDE and ICE, and the difference was statistically significant( z = -2.24, -4.84, t = 4.01, 6.54, P<0.05). In nasopharyngeal carcinoma, DJDE, ICE and NMSE of OF-DIR were superior to FD-DIR, and the difference was statistically significant ( t = 4.46, -7.49, z = -2.22, P<0.05), but there was no significant difference in JDM ( P>0.05). In lung cancer and nasopharyngeal carcinoma, JDNP of OF-DIR was worse than that of FD-DIR, and the difference was statistically significant ( z = -4.29, -4.02, P<0.01). In addition, DJDE is more specific and sensitive on ROC curve (AUC=0.77), and has different performance result for tumor images at different sites. Conclusions:The JDM and DJDE evaluation metrics proposed are effective for deformation registration algorithms. OF-DIR is suitable for both lung cancer and nasopharyngeal carcinoma, while the influence of organ motion on the registration effect should be considered when using FD-DIR.
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Objective:To investigate the effects of CT images reconstructed using different field of view (FOV) sizes on the automatic segmentation of organs at risk and dose calculation accuracy in radiotherapy after radical mastectomy.Methods:Under the same scanning conditions, CT values-electron density conversion curves were established by reconstructing the original CT images of a phantom placed at the isocenter and extended FOV (eFOV) positions using FOV sizes of 50, 60, 70 and 80 cm. Then, these curves were compared. A standard phantom with a known volume was scanned, and the automatic segmentation result of the phantom on CT images reconstructed using different FOV sizes was compared. A total of 30 patients in Guangdong Second Provincial General Hospital from January 2020 to June 2022 with breast cancer were randomly selected. Through simulated positioning, their CT images were reconstructed using different FOV sizes for the purpose of automatic segmentation of organs at risk, followed by comparison between the outcomes of automatic segmentation and physicians′segmentation. The treatment plan established based on CT images reconstructed using a FOV size of 50 cm (FOV 50 images for short) was applied to CT images reconstructed using FOV sizes of 60, 70 and 80 cm (FOV 60, FOV 70 and FOV 80 images for short) for dose calculation, and the dose calculation result were compared. Results:The CT values - electron density conversion curves derived from CT images reconstructed using different FOV sizes were roughly consistent. At the isocenter, the difference between the segmented volume and actual volume of the standard phantom increased up to a maximum of 6 cm 3 (4.8%) with an increase in the FOV size. As indicated by the automatic segmentation result, the segmentation accuracy of the spinal cord, trachea, esophagus, thyroid, healthy mammary gland, and skin decreased with an increase in the FOV size ( t = -28.43-8.23, P < 0.05). The comparison of dose calculated based on CT images reconstructed using different FOV sizes showed that there was no statistically significant differences( P>0.05) in the dose to target volume ( V95) and the maximum and average doses in the supraclavicular lymph node region, as well as the dose to organs at risk. The coverage for planned target volume decreased with an increase in the FOV size, with a maximum difference of 4.06%. Conclusions:It is recommended that, for radiotherapy after radical mastectomy, FOV 50 images should be selected for the automatic segmentation of organs at risk, CT-values-electron density conversion curves should be established based on the electron density phantom images of the eFOV region, and the eFOV 80 images should be preferred for dose calculation.
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Objective:To analyze the dosimetric differences of volumetric modulated arc therapy (VMAT) plans for lung cancer caused by different dose calculation algorithms and radiation field settings and thus to provide a reference for designing clinical VMAT plans for lung cancer.Methods:This study randomly selected 20 patients with lung cancer and divided them into four groups of VMAT plans, namely, a group adopting two fields and two arcs based on the AAA algorithm (2F2A_AAA), a group employing two fields and two arcs based on the AXB algorithm (2F2A_AXB), a group using two fields and two arcs based on the MC algorithm (2F2A_MC), and a group adopting one field and two arcs based on the MC algorithm (1F2A_MC). Then, this study evaluated the target coverage, high-dose control, dose homogeneity index (HI), conformity index (CI), and organs at risk (OARs) of the plans using different algorithms and radiation field settings.Results:The planning target volume (PTV) results of two fields combined with two arcs (2F2A) of three groups using different algorithms are as follows. 2F2A_MC achieved better results in both D1% and V 95% (the relative volume of the target volume surrounded by 95% of the prescribed dose) of planning gross target volume (PGTV) than 2F2A_AAA (D1%: t=-2.44, P=0.03; V95%:z=-2.04, P=0.04) and 2F2A_AXB (D1%: t=2.34, P=0.03; z=-3.21, P < 0.01). 2F2A_AXB outperformed 2F2A_AAA ( z=-3.66, P < 0.01) and was comparable to 2F2A_MC in terms of the CI of PGTV. Regarding OARs, 2F2A_AXB and 2F2A_MC decreased the V5 Gy of the whole lung by 0.68% ( z=-2.69, P=0.01) and 3.05% ( z=-3.52, P < 0.01), respectively compared to 2F2A_AAA. 2F2A_AXB achieved a whole-lung Dmean of 1776.44 cGy, which was superior to that of 2F2A_MC ( t=2.67, P=0.02) and 2F2A_AAA ( t=8.62, P < 0.01). Compared to 2F2A_AAA and 2F2A_MC, 2F2A_AXB decreased the V20 Gy of Body_5 mm by 1.45% ( z=-3.88, P < 0.01) and 2.01% ( z=-3.66, P < 0.01), respectively. The results of the two groups with different field settings showed that 1F2A_MC was superior to 2F2A_MC in both the CI of PTV1 and the HI of PTV2 (CI: t=2.61, P=0.02; HI: z=-2.20, P=0.03). Moreover, 1F2A_MC increased the Dmean of the whole lung by 26.29 cGy compared to 2F2A_MC ( t=2.28, P=0.04). Conclusions:Regarding the design of VMAT plans for lung cancer, the MC algorithm is suitable for the target priority and the AXB algorithm is suitable for the OAR priority. When only the MC algorithm is available, it is recommended to choose 1F2A in the case of target priority and select 2F2A in the case of OAR priority.