一种自适应的、解耦的表示方法用于多维MRI重建
An Adaptive, Disentangled Representation for Multidimensional MRI Reconstruction
AI总结：
本文提出了一种自适应解耦的多维MRI重建方法，通过学习特征表示和潜在扩散模型，实现无需任务特定训练的高效重建。
摘要：
我们提出了一种新的方法，用于表示和重建多维磁共振成像（MRI）数据。我们的方法基于一种新颖的、通过学习的基于特征的图像表示，将不同类型的特征，如几何和对比度，解耦到不同的低维潜在空间中，从而更好地利用多维图像中的特征相关性，并整合针对不同特征类型的预学习先验知识进行重建。更具体地说，解耦是通过编码器-解码器网络和使用大规模公开数据的图像迁移训练实现的，通过基于风格的解码器设计得到增强。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。开发了新的重建公式和算法，将学习到的表示与零样本自监督学习适应和子空间建模相结合。所提出的方法已在加速T1和T2参数映射上进行了评估，实现了优于现有最先进重建方法的性能，无需任务特定的监督训练或微调。这项工作提供了一种新的基于学习的多维图像重建策略，其中仅限于可用于问题特定或任务特定训练的数据。
英文摘要：
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.
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