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：我们提出了E(3)-Pose，一种新颖的快速姿态估计方法，该方法联合并显式地建模旋转等变性与物体对称性。我们的研究源于诊断性MRI扫描中胎儿头部运动跟踪这一具有挑战性的问题。我们旨在通过6自由度头部姿态估计实现诊断性二维MRI切片的自动自适应规划，并由每次二维切片采集前获取的快速低分辨率三维MRI体素提供支持。由于解剖学固有对称性导致的姿态模糊性，以及低分辨率、噪声和伪影的影响，现有姿态估计方法难以泛化至临床体素。相比之下，E(3)-Pose通过结构设计捕获解剖对称性与刚体姿态等变性，并能生成稳健的胎儿头部姿态估计。我们在公开和具有代表性的临床胎儿MRI数据集上的实验表明，该方法在跨领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体素上达到了最先进的精度，为未来临床转化提供了支持。我们的代码已开源发布在github.com/MedicalVisionGroup/E3-Pose。