Implicit visual knowledge in a large latent diffusion model (LLDM) pre-trained on natural images is rich and hypothetically universal to natural and medical images. To test this hypothesis from a practical perspective, we propose a novel framework for undersampled MRI Reconstruction by Prompting a large latent Diffusion model (MRPD). While the existing methods trained on MRI datasets are typically of limited generalizability toward diverse data acquisition scenarios, MRPD supports unsupervised and universally adaptive MRI reconstruction. For unsupervised reconstruction, MRSampler guides LLDM with a random-phase-modulated hard-to-soft control. With any single- or multiple-source MRI dataset, MRPD's performance is boosted universally by a lightweight MRAdapter that only finetunes the LLDM's autoencoder. Experiments on FastMRI and IXI show that MRPD is the only model that supports both MRI database-free and database-available scenarios and attains the best generalizability towards out-of-domain (OOD) samplings, contrasts, and organs among compared unsupervised, supervised, and MRI diffusion methods. To our knowledge, MRPD is the first method that empirically shows the universal prowess of an LLDM pre-trained on vast natural images for MRI. Our official implementation is at https://github.com/Z7Gao/MRPD.

翻译：在自然图像上预训练的大型潜在扩散模型（LLDM）所蕴含的隐式视觉知识是丰富的，并且假设其对自然图像与医学图像具有普适性。为从实践角度验证这一假设，我们提出了一种新颖的框架：通过提示大型潜在扩散模型进行欠采样磁共振成像重建（MRPD）。现有基于MRI数据集训练的方法通常对多样化的数据采集场景泛化能力有限，而MRPD支持无监督且具有普适适应性的MRI重建。对于无监督重建，MRSampler通过随机相位调制的硬到软控制来引导LLDM。利用任何单源或多源MRI数据集，仅需对LLDM的自编码器进行轻量级微调的MRAdapter即可普遍提升MRPD的性能。在FastMRI和IXI数据集上的实验表明，MRPD是唯一同时支持无MRI数据库和有MRI数据库两种场景的模型，并且在所比较的无监督、有监督及MRI扩散方法中，对域外（OOD）采样方式、对比度和器官具有最佳的泛化能力。据我们所知，MRPD是首个通过实验证明，在大量自然图像上预训练的LLDM对MRI具有普适性能力的方法。我们的官方实现位于 https://github.com/Z7Gao/MRPD。