Tagged MRI enables tracking internal tissue motion non-invasively. It encodes motion by modulating anatomy with periodic tags, which deform along with tissue. However, the entanglement between anatomy, tags and motion poses significant challenges for post-processing. The existence of tags and imaging blur hinders downstream tasks such as segmenting anatomy. Tag fading, due to T1-relaxation, disrupts the brightness constancy assumption for motion tracking. For decades, these challenges have been handled in isolation and sub-optimally. In contrast, we introduce a blind and nonlinear inverse framework for tagged MRI that, for the first time, unifies these tasks: anatomical image recovery, high-resolution cine image synthesis, and motion estimation. At its core, the synergy of MR physics and generative priors enables us to blindly estimate the unknown forward imaging models and high-resolution underlying anatomy, while simultaneously tracking 3D diffeomorphic Lagrangian motion over time. Experiments on tagged brain MRI demonstrate that our approach yields high-resolution anatomy images, cine images, and more accurate motion than specialized methods.

翻译：标记磁共振成像能够无创地追踪内部组织运动。该技术通过周期性标记图案对解剖结构进行调制来编码运动信息，标记图案会随组织一同形变。然而，解剖结构、标记图案与运动之间的相互纠缠给后处理带来了重大挑战。标记图案的存在与成像模糊会阻碍解剖结构分割等下游任务。由T1弛豫导致的标记衰减则会破坏运动追踪所依赖的亮度恒定性假设。数十年来，这些挑战始终被孤立且次优地处理。相比之下，我们提出了一种针对标记磁共振成像的盲非线性逆问题求解框架，首次实现了以下任务的统一：解剖图像恢复、高分辨率动态电影图像合成及运动估计。该框架的核心在于，通过磁共振物理原理与生成先验的协同作用，我们能够盲估计未知的前向成像模型与高分辨率底层解剖结构，同时追踪三维微分同胚拉格朗日运动随时间的变化。在脑部标记磁共振成像上的实验表明，相较于专用方法，本方法能够获得更高分辨率的解剖图像、动态电影图像以及更精确的运动估计结果。