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.

翻译：近年来，医学影像生成模型在表征多模态数据方面展现出巨大潜力。然而，不同数据集中模态可用性的差异限制了所生成合成数据的普适性。为解决这一问题，我们提出了一种新颖的物理信息生成模型，能够合成可变数量的脑部MRI模态，包括原始数据集中未出现的模态。我们的方法采用潜在扩散模型和两步生成流程：首先利用潜在扩散模型合成未观测的物理组织特性图，随后将这些特性图与物理信号模型相结合以生成最终MRI扫描。实验结果表明，该方法在生成未见过的MR对比度及保持物理合理性方面具有显著效果。此外，我们通过将生成的组织特性分布与真实脑组织测量结果进行对比，验证了其分布特性。