MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice. Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions. However, most diffusion-based MRI synthesis models are using a single modality. As they operate in the original image domain, they are memory-intensive and less feasible for multi-modal synthesis. Moreover, they often fail to preserve the anatomical structure in MRI. Further, balancing the multiple conditions from multi-modal MRI inputs is crucial for multi-modal synthesis. Here, we propose the first diffusion-based multi-modality MRI synthesis model, namely Conditioned Latent Diffusion Model (CoLa-Diff). To reduce memory consumption, we design CoLa-Diff to operate in the latent space. We propose a novel network architecture, e.g., similar cooperative filtering, to solve the possible compression and noise in latent space. To better maintain the anatomical structure, brain region masks are introduced as the priors of density distributions to guide diffusion process. We further present auto-weight adaptation to employ multi-modal information effectively. Our experiments demonstrate that CoLa-Diff outperforms other state-of-the-art MRI synthesis methods, promising to serve as an effective tool for multi-modal MRI synthesis.

翻译：MRI合成有望缓解临床实践中MRI模态缺失的挑战。扩散模型通过建模复杂多变的数据分布，已成为图像合成的有效技术。然而，大多数基于扩散的MRI合成模型仅使用单一模态。由于它们在原始图像域中运行，内存消耗大，且难以实现多模态合成。此外，这些模型往往无法保留MRI中的解剖结构。同时，平衡多模态MRI输入中的多个条件对于多模态合成至关重要。本文首次提出基于扩散的多模态MRI合成模型，即条件潜在扩散模型（CoLa-Diff）。为减少内存消耗，我们将CoLa-Diff设计为在潜在空间中运行。我们提出了一种新颖的网络架构（例如类似协同滤波的方法），以解决潜在空间中可能出现的压缩和噪声问题。为了更好地保持解剖结构，引入脑区域掩膜作为密度分布先验来引导扩散过程。我们进一步提出自适应权重调整以有效利用多模态信息。实验表明，CoLa-Diff优于其他最先进的MRI合成方法，有望成为多模态MRI合成的有效工具。