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 2D diagnostic MRI slices with 6-DoF head pose estimation, supported by 3D MRI volumes rapidly acquired before each 2D slice. Existing 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, paving the way for clinical translation. Our implementation is available at github.com/ramyamut/E3-Pose.

翻译：我们提出E(3)-Pose，一种新颖的快速姿态估计方法，能够联合且显式地建模旋转等变性与物体对称性。本研究的动机源于诊断性MRI扫描中胎儿头部运动补偿这一具有挑战性的问题。我们的目标是通过在每次二维切片采集前快速获取的三维MRI体积数据的支持，实现基于六自由度头部姿态估计的二维诊断性MRI切片自动自适应定位。现有方法由于固有解剖对称性引起的姿态歧义，以及低分辨率、噪声和伪影等问题，难以泛化至临床数据。相比之下，E(3)-Pose通过结构设计捕获解剖对称性和刚性姿态等变性，从而实现对胎儿头部姿态的鲁棒估计。我们在公开可获取且具有代表性的临床胎儿MRI数据集上的实验表明，该方法在不同领域均展现出卓越的鲁棒性和泛化能力。至关重要的是，E(3)-Pose在临床MRI体积数据上达到了最先进的精度，为临床转化铺平了道路。我们的实现代码发布于github.com/ramyamut/E3-Pose。