Image synthesis of effective atomic number images using a deep convolutional neural network-based generative adversarial network

Abstract

Materials and methods:The image synthesis framework to obtain the effective atomic number images from a single-energy CT image at 120 kVp using a CNN-based GAN was developed. The evaluation metrics were the mean absolute error (MAe), relative root mean square error (rMse), relative mean square error (Mse), structural similarity index (ssIM), peak signal-to-noise ratio (psNr), and mutual information (MI). results:The difference between the reference and synthetic effective atomic numbers was within 9.7% in all regions of interest. The averages of MAe, rMse, Mse, ssIM, psNr, and MI of the reference and synthesized images in the test data were 0.09, 0.045, 0.0, 0.89, 54.97, and 1.03, respectively. conclusions: In this study, an image synthesis framework using single-energy CT images was constructed to obtain atomic number images scanned by DeCT. This image synthesis framework can aid in material decomposition without extra scans in DeCT.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

“…MAE, PSNR, and SSIM were employed to compare Gen-VMI 40keV and VMI 40keV in this study. These three metrics are commonly used for image quality assessment in the field of image processing, and may help measure the similarity between Gen-VMI 40keV and VMI 40keV , with SSIM showing a correlation with the perceived quality within the context of the human visual system [35][36][37]. The results revealed that all models achieved PSNR and SSIM values above 40 and 0.98, respectively, in the validation dataset.…”