ABSTRACT
OBJECTIVE@#To propose a framework that combines sinogram interpolation with unsupervised image-to-image translation (UNIT) network to correct metal artifacts in CT images.@*METHODS@#The initially corrected CT image and the prior image without artifacts, which were considered as different elements in two different domains, were input into the image transformation network to obtain the corrected image. Verification experiments were carried out to assess the effectiveness of the proposed method using the simulation data, and PSNR and SSIM were calculated for quantitative evaluation of the performance of the method.@*RESULTS@#The experiment using the simulation data showed that the proposed method achieved better results for improving image quality as compared with other methods, and the corrected images preserved more details and structures. Compared with ADN algorithm, the proposed algorithm improved the PSNR and SSIM by 2.4449 and 0.0023 when the metal was small, by 5.9942 and 8.8388 for images with large metals, and by 8.8388 and 0.0130 when both small and large metals were present, respectively.@*CONCLUSION@#The proposed method for metal artifact correction can effectively remove metal artifacts, improve image quality, and preserve more details and structures on CT images.
Subject(s)
Artifacts , Algorithms , Computer Simulation , Tomography, X-Ray ComputedABSTRACT
Objective@#To quantitatively evaluate dose accuracy of radiotherapy for cervical cancer.@*Methods@#A CT image correction algorithm based on image transformation was proposed. Referring to CBCT images, CT images of radiotherapy plan for cervical cancer were corrected to obtain the corrected images which could reflect the actual body position of treatment. The clinical plan was transplanted to the corrected images for dose recalculation as a test plan, and the dosimetry parameters were statistically compared to evaluate the dose accuracy.@*Results@#Both of the target coverage of contrast plans could meet the clinical requirements (>98%), and there was no significant difference in the homogeneity index (P=0.150). The conformability of the test plan was significantly worse than that of the clinical plan (P<0.05). The maximum dose of each organ at risk in the test plan was approximately 30 cGy higher than that of the clinical plan (P<0.05), V50 was slightly higher than that of the clinical plan, whereas the average dose (Dmean) did not significantly differ.@*Conclusion@#The CT image correction algorithm based on image transformation can quantitatively evaluate the dose accuracy of radiotherapy for cervical cancer, which provides reference for resolving similar problems in clinical practice.