Tagged magnetic resonance imaging~(MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields. To address these issues, this paper presents a novel unsupervised phase-based 3D motion estimation technique for tagged MRI. We introduce two key innovations. First, we apply a sinusoidal transformation to the harmonic phase input, which enables end-to-end training and avoids the need for phase interpolation. Second, we propose a Jacobian determinant-based learning objective to encourage incompressible flow fields for deforming biological tissues. Our method efficiently estimates 3D motion fields that are accurate, dense, and approximately diffeomorphic and incompressible. The efficacy of the method is assessed using human tongue motion during speech, and includes both healthy controls and patients that have undergone glossectomy. We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion. The code is available: https://github.com/jasonbian97/DRIMET-tagged-MRI

翻译：标记磁共振成像用于观测和量化变形组织的精细运动已达数十年。然而，该技术面临标记衰减、大范围运动、计算时间长以及难以获取微分同胚不可压缩流场等挑战。针对这些问题，本文提出一种新颖的无监督相位基三维运动估计技术。我们引入两项关键创新：首先，对谐波相位输入应用正弦变换，实现端到端训练并避免相位插值需求；其次，提出基于雅可比行列式的学习目标，以促进生物组织变形的不可压缩流场。该方法能高效估计精确、稠密且近似微分同胚与不可压缩的三维运动场。通过人体说话时的舌部运动评估方法有效性，对象包括健康对照组和接受过舌切除术的患者。实验表明，该方法优于现有技术，同时在速度、对标记衰减的鲁棒性及大范围舌部运动处理方面均有提升。代码开源：https://github.com/jasonbian97/DRIMET-tagged-MRI