Synthesize conventional MRI Sequences by Generative Adversarial Networks with only T2 for Use in a Multisequence gliomas classification Model

The aim of this study was to test deep learning classification models of glioma subtypes using the generated images. GANs were created based on the two frameworks, pix2pix and cycleGAN. The source domain was T2 and the target domain was T1c, T2-FLAIR or ADC. The results demonstrated that the T2 to T1c pix2pix model has the highest PSNR and SSI. When only the T2-flair or T1c sequence is replaced with the generated image, the classification accuracy is same as the original image. Therefore, depending solely on T2 sequences, GANs networks could generate other sequences for Use in gliomas classification Model.