We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our work is motivated by the challenging problem of accounting for fetal head motion during a diagnostic MRI scan. We aim to enable automatic adaptive prescription of diagnostic 2D MRI slices with 6-DoF head pose estimation, supported by rapid low-resolution 3D MRI volumes acquired before each 2D slice. Existing pose estimation methods struggle to generalize to clinical volumes due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and artifacts. In contrast, E(3)-Pose captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method across domains. Crucially, E(3)-Pose achieves state-of-the-art accuracy on clinical MRI volumes, supporting future clinical translation. Our implementation is publicly available at github.com/MedicalVisionGroup/E3-Pose.

翻译：摘要：我们提出E(3)-Pose——一种新颖的快速姿态估计方法，该方法联合且显式地建模了旋转等变性与对象对称性。本研究源于诊断性MRI扫描中胎儿头部运动建模这一挑战性问题。我们旨在通过快速低分辨率3D MRI体数据（在每个2D切片采集前获取）支撑的6自由度头部姿态估计，实现诊断性2D MRI切片的自动自适应规划。现有姿态估计方法因内在解剖对称性导致的姿态模糊性，以及低分辨率、噪声和伪影等限制，难以泛化至临床体数据。相比之下，E(3)-Pose通过构造性设计捕捉解剖对称性与刚体姿态等变性，并对胎儿头部姿态产生鲁棒估计。我们在公开及代表性临床胎儿MRI数据集上的实验表明，该方法在跨域场景中具有卓越的鲁棒性和泛化能力。关键的是，E(3)-Pose在临床MRI体数据上达到当前最优精度，为未来的临床转化提供支撑。我们的实现代码已开源至github.com/MedicalVisionGroup/E3-Pose。