Cross-modality 3D MRI synthesis via cycle-guided denoising diffusion probability model

We propose a deep learning framework, the cycle-guided denoising diffusion probability model (CG-DDPM), for cross-modality magnetic resonance imaging (MRI) synthesis. The CG-DDPM aims to generate high-quality MRIs of a target modality from an existing modality, addressing the challenge of missing MRI sequences in clinical practice.

The CG-DDPM employs two interconnected conditional diffusion probabilistic models, with a cycle-guided reverse latent noise regularization to enhance synthesis consistency and anatomical fidelity. The framework was evaluated using the BraTS2020 dataset, which includes three-dimensional brain MRIs with $T1$ -weighted, $T2$ -weighted, and FLAIR modalities. The synthetic images were quantitatively assessed using metrics such as multi-scale structural similarity measure (MSSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE). The CG-DDPM was benchmarked against state-of-the-art methods, including IDDPM, IDDIM, and MRI-cGAN.

The CG-DDPM demonstrated superior performance across all cross-modality synthesis tasks (T1 → T2, T2 → T1, T1 → FLAIR, and FLAIR → T1). It consistently achieved the highest MSSIM values (ranging from 0.966 to 0.971), the lowest MAE (0.011 to 0.013), and competitive PSNR values (27.7 to 28.8 dB). Across all tasks, CG-DDPM outperformed IDDPM, IDDIM, and MRI-cGAN in most metrics and exhibited significantly lower uncertainty and inconsistency in MC-based sampling. Statistical analyses confirmed the robustness of CG-DDPM, with $p\text{-}\text{values}<0.05$ in key comparisons.

The proposed CG-DDPM provides a robust and efficient solution for cross-modality MRI synthesis, offering improved accuracy, stability, and clinical applicability compared with existing methods. This approach has the potential to streamline MRI-based workflows, enhance diagnostic imaging, and support precision treatment planning in medical physics and radiation oncology.