T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks

Abstract

background: The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GaN) in T1-weighted and T2-weighted magnetic resonance imaging (MrI) images. Materials and methods: a total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MrI images and T2-weighted to T1-weighted MrI images were created with GaN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256. results: The relative mean absolute error (rMae) was 0.15 for T1-weighted to T2-weighted MrI images and 0.17 for T2-weighted to T1-weighted MrI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMse) and root mean square error (rrMse), and the largest peak signal-to-noise ratio (psNr) and mutual information (MI). The computation time depended on the image size. conclusions: Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MrI tests without the need for prolonged examinations.