Cross-modality brain image translation using CycleGAN for MRI to CT and CT to MRI conversion

Abstract

Accurate cross-modality brain image translation between magnetic resonance imaging (MRI) and computed tomography (CT) can enhance diagnosis and treatment planning by combining complementary structural and tissue information. This study develops a cycle-consistent generative adversarial network (CycleGAN)-based framework for unpaired MRI-to-CT and CT-to-MRI conversion, eliminating the need for paired datasets. Publicly available brain imaging datasets were preprocessed with intensity normalization and resizing, and the model was trained using a U-Net generator for anatomical preservation and a PatchGAN discriminator for texture fidelity. Quantitative evaluation using structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) demonstrated that the proposed method outperforms baseline generative adversarial network (GAN) architectures, while expert radiologist reviews confirmed high structural consistency. The approach effectively maintains anatomical integrity and visual realism across modalities. This work’s novelty lies in optimizing CycleGAN architecture and hyperparameters for brain imaging translation, achieving superior performance without paired scans. The findings indicate potential for integration into clinical workflows, particularly in multimodal diagnosis and radiotherapy planning, offering a pathway to reduce scan redundancy and improve patient care. Unlike prior unpaired image-translation studies, this work introduces a U-Net–enhanced CycleGAN with optimized loss weighting and architecture tailoring for brain imaging, achieving improved structural fidelity and computational efficiency.