统一恢复解剖结构、生成高清动态图像并精确追踪运动，解决标记MRI长期难题。
Solving a Nonlinear Blind Inverse Problem for Tagged MRI with Physics and Deep Generative Priors
- 融合医学成像物理模型与生成先验，盲解非线性逆问题
- 在脑部标记MRI上实现高清解剖图、动态图像和更准运动追踪
- 适合医学影像重建与运动分析研究者参考
标记MRI可无创追踪组织内部运动，通过周期性标记调制解剖结构，其形变随组织同步。然而，解剖结构、标记与运动之间的纠缠给后处理带来挑战：标记存在及成像模糊影响解剖分割；因T1弛豫导致的标记衰减破坏运动追踪中的亮度恒定假设。数十年来，这些问题被孤立处理且效果不佳。本文首次提出一种盲式非线性逆框架，统一完成解剖图像恢复、高分辨率动态图像合成与运动估计。核心在于结合磁共振物理模型与生成先验，可盲估未知前向成像模型与高分辨率原始解剖结构，同时在时间上同步追踪三维微分同胚拉格朗日运动。在标记脑部MRI上的实验表明，本方法生成的解剖图像、动态图像分辨率更高，运动估计精度优于专用方法。
原文摘要 · Abstract (English)
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.
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