Abstract
Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that deforms with the tissue. Due to longitudinal relaxation and progression to steady-state, the tags and tissue brightnesses change over time, which makes tracking with optical flow methods error-prone. Although Fourier methods can alleviate these problems, they are also sensitive to brightness changes as well as spectral spreading due to motion. To address these problems, we introduce the brightness-invariant tracking estimation (BRITE) technique for tagged MRI. BRITE disentangles the anatomy from the tag pattern in the observed tagged image sequence and simultaneously estimates the Lagrangian motion. The inherent ill-posedness of this problem is addressed by leveraging the expressive power of denoising diffusion probabilistic models to represent the probabilistic distribution of the underlying anatomy and the flexibility of physics-informed neural networks to estimate biologically-plausible motion. A set of tagged MR images of a gel phantom was acquired with various tag periods and imaging flip angles to demonstrate the impact of brightness variations and to validate our method. The results show that BRITE achieves more accurate motion and strain estimates as compared to other state of the art methods, while also being resistant to tag fading.
Abstract (translated)
磁共振标记（MR 标记）是一种用于非侵入性追踪活体内组织运动的成像技术，通过创建一个可视化的磁饱和模式（标签），该模式会随着组织变形而变化。由于纵向弛豫和向稳态发展的过程，标签及组织亮度随时间发生变化，这使得使用光学流方法进行跟踪容易产生误差。虽然傅立叶变换可以缓解这些问题，但它们对亮度变化同样敏感，并且会因运动导致频谱扩散问题。为了应对这些挑战，我们引入了一种名为亮度不变追踪估计（BRITE）的技术用于标记 MRI。BRITE 技术能够从观察到的标记图像序列中分离出解剖结构和标签模式，并同时估算拉格朗日运动。通过利用去噪扩散概率模型来表示潜在解剖结构的概率分布以及采用物理信息神经网络来估算生物合理性运动，这种方法解决了该问题内在的不适定性。我们使用一组不同标记周期和成像翻转角获得的凝胶模型标记 MR 图像数据集，以演示亮度变化的影响并验证我们的方法的有效性。实验结果表明，与现有的先进技术相比，BRITE 能够提供更准确的运动和应变估计，并且对标签褪色具有更强的抗干扰能力。
URL
https://arxiv.org/abs/2505.18365
