用物理模型生成缺失的脑部MRI模态，提升合成数据通用性。
Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis
- 基于潜在扩散模型先生成组织物理属性图
- 结合物理信号模型合成多模态MRI，支持未见对比度
- 生成结果符合真实组织分布，适合医学研究应用
近期生成模型在医学影像多模态表示方面取得进展，但不同数据集间模态可用性的差异限制了合成数据的通用性。为此，我们提出一种新型物理信息引导的生成模型，可合成任意数量的脑部MRI模态，包括原始数据中不存在的模态。该方法采用潜在扩散模型与两步生成流程：首先利用潜在扩散模型生成未观测到的组织物理属性图，再将其与物理信号模型结合，生成最终的MRI图像。实验表明，该方法能有效生成未见的MR对比度，并保持物理合理性。此外，通过对比真实脑组织测量值，验证了生成组织属性分布的真实性。
原文摘要 · Abstract (English)
Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To address this, we present a novel physics-informed generative model capable of synthesizing a variable number of brain MRI modalities, including those not present in the original dataset. Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan. Our experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility. Furthermore, we validate the distributions of generated tissue properties by comparing them to those measured in real brain tissue.
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