Reg-APGAN: Registration-Guided Anatomy-Preserving GAN for CT-to-MR Translation

Computed tomography (CT) is widely accessible for abdominal imaging but provides limited soft-tissue contrast compared with magnetic resonance (MR) imaging. Cross-modality synthesis offers a potential means to virtually complete multimodal protocols, yet current CT-to-MR translation methods often prioritize perceptual realism over anatomical reliability, resulting in geometric distortion and limited downstream utility. To address this gap, we introduce Reg-APGAN, a registration-guided, anatomy-preserving framework that synthesizes MR-like contrast from CT while constraining organ geometry. Using abdominal CT and T1-weighted MR from 114 patients, we convert both modalities into a unified coronal space and perform hierarchical rigid registration using skeletal and multi-organ labels. Reg-APGAN then performs 2D slice-wise translation on rigidly aligned 3D CT-MR volumes under structure-aware supervision to preserve anatomical topology. Evaluation is performed on the full abdominal cavity, a highly deformable and challenging setting for multimodal synthesis. Under identical weak-alignment conditions, Reg-APGAN yields a 0.51 dB PSNR improvement, the highest MS-SSIM, a 6-7% reduction in MAE relative to CycleGAN, and a 13-15% reduction in ROI-based intensity and distributional errors. Moreover, pseudo-MR images generated by Reg-APGAN enabled more anatomically coherent downstream segmentation than CT alone and pseudo-MR images generated by baseline translation methods. By coupling anatomical registration with contrast-level translation, Reg-APGAN enables structurally consistent MR-like visualization from CT and supports multimodal AI development. However, the method is not intended to replace diagnostic MR and requires external validation and radiologist reader studies and external datasets is required.