We present a new approach for representing and reconstructing multidimensional magnetic resonance imaging (MRI) data. Our method builds on a novel, learned feature-based image representation that disentangles different types of features, such as geometry and contrast, into distinct low-dimensional latent spaces, enabling better exploitation of feature correlations in multidimensional images and incorporation of pre-learned priors specific to different feature types for reconstruction. More specifically, the disentanglement was achieved via an encoderdecoder network and image transfer training using large public data, enhanced by a style-based decoder design. A latent diffusion model was introduced to impose stronger constraints on distinct feature spaces. New reconstruction formulations and algorithms were developed to integrate the learned representation with a zero-shot selfsupervised learning adaptation and subspace modeling. The proposed method has been evaluated on accelerated T1 and T2 parameter mapping, achieving improved performance over state-of-the-art reconstruction methods, without task-specific supervised training or fine-tuning. This work offers a new strategy for learning-based multidimensional image reconstruction where only limited data are available for problem-specific or task-specific training.

翻译：本文提出了一种用于表示和重建多维磁共振成像（MRI）数据的新方法。该方法基于一种新颖的、基于学习特征的表征方式，将不同类型的特征（如几何结构与对比度）解耦至不同的低维潜在空间，从而能够更好地利用多维图像中的特征相关性，并在重建过程中融入针对不同特征类型的预学习先验知识。具体而言，这种解耦是通过编码器-解码器网络以及利用大规模公共数据进行的图像迁移训练实现的，并通过基于风格的解码器设计加以增强。我们引入了潜在扩散模型，以对不同的特征空间施加更强的约束。本文开发了新的重建公式与算法，将学习到的表征与零样本自监督学习自适应及子空间建模相结合。所提方法已在加速的T1和T2参数映射任务上进行了评估，其性能优于当前最先进的重建方法，且无需针对特定任务的有监督训练或微调。这项工作为基于学习的多维图像重建提供了一种新策略，尤其适用于仅有有限数据可用于问题特定或任务特定训练的场景。