Output Steering for Latent Diffusion-Based MRI-to-CT Synthesis

Abstract

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are fundamental imaging modalities that provide complementary information for clinical assessment. However, CT acquisition may not always be preferred or available due to additional cost, workflow burden, and exposure to ionizing radiation. Therefore, reliably synthesizing a corresponding CT image from an available MRI scan is an important medical imaging problem. In this work, we present a conditional latent diffusion framework for MRI-to-CT synthesis. To improve the reliability of stochastic generation, the proposed framework incorporates a modular output steering mechanism that favors candidates better matched to target-domain characteristics. In this way, the diversity of diffusion-based synthesis is preserved while output consistency and realism are improved. Experimental results indicate that the proposed approach yields improved quantitative and qualitative performance over a baseline latent diffusion model. The proposed method achieved 26.49±2.19 dB PSNR and 87.89±2.59% SSIM, outperforming the baseline latent diffusion model.