In this paper, we introduce SpaER, a pioneering method for fetal motion tracking that leverages equivariant filters and self-attention mechanisms to effectively learn spatio-temporal representations. Different from conventional approaches that statically estimate fetal brain motions from pairs of images, our method dynamically tracks the rigid movement patterns of the fetal head across temporal and spatial dimensions. Specifically, we first develop an equivariant neural network that efficiently learns rigid motion sequences through low-dimensional spatial representations of images. Subsequently, we learn spatio-temporal representations by incorporating time encoding and self-attention neural network layers. This approach allows for the capture of long-term dependencies of fetal brain motion and addresses alignment errors due to contrast changes and severe motion artifacts. Our model also provides a geometric deformation estimation that properly addresses image distortions among all time frames. To the best of our knowledge, our approach is the first to learn spatial-temporal representations via deep neural networks for fetal motion tracking without data augmentation. We validated our model using real fetal echo-planar images with simulated and real motions. Our method carries significant potential value in accurately measuring, tracking, and correcting fetal motion in fetal MRI sequences.

翻译：本文提出了一种开创性的胎儿运动追踪方法SpaER，该方法利用等变滤波器和自注意力机制有效学习时空表示。与从图像对静态估计胎儿脑运动的传统方法不同，我们的方法在时间和空间维度上动态追踪胎儿头部的刚性运动模式。具体而言，我们首先构建了一个等变神经网络，通过图像的低维空间表示高效学习刚性运动序列。随后，我们通过融入时间编码和自注意力神经网络层来学习时空表示。该方法能够捕捉胎儿脑运动的长期依赖性，并解决由对比度变化和严重运动伪影引起的配准误差。我们的模型还提供了几何形变估计，可正确处理所有时间帧间的图像畸变。据我们所知，我们的方法是首个无需数据增强、通过深度神经网络学习时空表示以进行胎儿运动追踪的研究。我们使用模拟和真实运动的胎儿平面回波图像验证了模型。该方法在精确测量、追踪和校正胎儿MRI序列中的胎儿运动方面具有重要的潜在价值。