标题： 一种自监督扩散桥用于MRI重建 显示英文标题

标题： A Self-supervised Diffusion Bridge for MRI Reconstruction

摘要： 扩散桥（DBs）是一类扩散模型，通过在两个配对的图像分布之间进行插值，实现更快的采样。 训练传统的DBs进行图像重建需要高质量的参考图像，这限制了它们在没有此类参考图像的场景中的应用。 我们提出SelfDB作为一种新颖的自监督方法，在无需任何高质量参考图像的情况下，直接在可用的噪声测量数据上训练DBs。 SelfDB通过进一步对可用测量数据进行两次额外的子采样，并使用可用测量数据作为训练目标，训练一个神经网络来逆转相应的退化过程。 我们在压缩感知MRI上验证了SelfDB，结果表明其性能优于去噪扩散模型。 显示英文摘要

摘要： Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions. Training traditional DBs for image reconstruction requires high-quality reference images, which limits their applicability to settings where such references are unavailable. We propose SelfDB as a novel self-supervised method for training DBs directly on available noisy measurements without any high-quality reference images. SelfDB formulates the diffusion process by further sub-sampling the available measurements two additional times and training a neural network to reverse the corresponding degradation process by using the available measurements as the training targets. We validate SelfDB on compressed sensing MRI, showing its superior performance compared to the denoising diffusion models.