Multi-contrast magnetic resonance imaging (MRI) is the most common management tool used to characterize neurological disorders based on brain tissue contrasts. However, acquiring high-resolution MRI scans is time-consuming and infeasible under specific conditions. Hence, multi-contrast super-resolution methods have been developed to improve the quality of low-resolution contrasts by leveraging complementary information from multi-contrast MRI. Current deep learning-based super-resolution methods have limitations in estimating restoration uncertainty and avoiding mode collapse. Although the diffusion model has emerged as a promising approach for image enhancement, capturing complex interactions between multiple conditions introduced by multi-contrast MRI super-resolution remains a challenge for clinical applications. In this paper, we propose a disentangled conditional diffusion model, DisC-Diff, for multi-contrast brain MRI super-resolution. It utilizes the sampling-based generation and simple objective function of diffusion models to estimate uncertainty in restorations effectively and ensure a stable optimization process. Moreover, DisC-Diff leverages a disentangled multi-stream network to fully exploit complementary information from multi-contrast MRI, improving model interpretation under multiple conditions of multi-contrast inputs. We validated the effectiveness of DisC-Diff on two datasets: the IXI dataset, which contains 578 normal brains, and a clinical dataset with 316 pathological brains. Our experimental results demonstrate that DisC-Diff outperforms other state-of-the-art methods both quantitatively and visually.

翻译：多对比度磁共振成像（MRI）是基于脑组织对比度表征神经系统疾病最常用的管理工具。然而，获取高分辨率MRI扫描耗时且在某些特定条件下难以实现。为此，研究者开发了多对比度超分辨率方法，通过利用多对比度MRI的互补信息提升低分辨率对比度的质量。当前基于深度学习的超分辨率方法在估计重建不确定性和避免模式崩溃方面存在局限性。尽管扩散模型已成为图像增强领域的前沿方法，但如何捕捉多对比度MRI超分辨率中多条件引入的复杂交互作用仍是临床应用的挑战。本文提出一种解耦条件扩散模型DisC-Diff，用于多对比度脑部MRI超分辨率。该模型利用扩散模型的采样生成机制和简洁目标函数，有效估计重建过程中的不确定性并确保优化过程稳定。此外，DisC-Diff通过解耦多流网络充分挖掘多对比度MRI的互补信息，提升模型在多条件多对比度输入下的解释能力。我们在两个数据集上验证了DisC-Diff的有效性：包含578例正常脑部的IXI数据集，以及包含316例病理脑部的临床数据集。实验结果表明，DisC-Diff在定量指标和视觉质量上均优于现有最优方法。