Dosimetric effect of CT truncated regionson radiotherapy for thoracic esophageal cancer / 中国辐射卫生

@#<b>Objective</b> To investigate the dosimetric effect of truncated regions in computed tomography (CT) images on the targets and organs at risk in volumetric modulated arc therapy (VMAT) for middle thoracic esophageal cancer. <b>Methods</b> CT images of 15 patients with middle thoracic esophageal cancer were selected. Circle masks were used to make the volume of the truncated region account for 10%, 20%, 30%, and 40% of the arm volume, and the corresponding truncated CT images were obtained. The real CT was denoted as CT0. Two radiotherapy plans were made on CT0. One plan was VMAT_1F with full arcs, and the other one was VMAT_3F with arm avoidance. The plans were transplanted to four truncated CT, respectively, and the dosimetric differences between different plans were compared using Wilcoxon signed-rank test. <b>Results</b> Compared with VMAT_1F in CT0, <i>D</i>_mean and <i>V</i>₅ of the lung decreased in VMAT_3F, but <i>D</i>_max of the spinal cord, <i>D</i>_mean of the heart, and <i>V</i>₂₀ of the lung increased. In VMAT_3F, there was no statistically significant difference between the dosimetric parameters in the four truncated CT and those in CT0 (all <i>P</i> > 0.05). In VMAT_1F, except for homogeneity index and <i>D</i>_max of the spinal cord, the dosimetric parameters in four truncated CT were significantly different from those in CT0 (<i>P</i> < 0.05). The dosimetric difference increased with the increase in truncated region-to-volume ratio. <b>Conclusion</b> Complete CT data should be collected in clinical practice, and the radiation field avoiding the truncated regionshould be set if necessary to reduce the influence of the truncated region on dosimetry.

Research progress of MR imaging for prediction of CT imaging / 中国辐射卫生

Medical images can provide clinicans with accurate and comprehensive patients’ information. Morphological or functional abnormalities caused by various diseases can be manifested in many aspects. Although MR images and CT images can highlight the medical image data of different tissue structures of patients, single MR images or CT images cannot fully reflect the complexity of diseases. Using MR image to predict CT image is one of the cross-modal prediction of medical images. In this paper, the methods of MR image prediction for CTmage are classified into four categoriesincluding registration based on atlas, based on image segmentationmethod, based on learning method and based on deep learning method. In our research, we concluded that the method based on deep learning should bemore promoted in the future by compering the existing problems and future development of MR image predicting CT image method.