SpaER：学习用于胎儿脑运动跟踪的时空等变表示
SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking
AI总结：
本文提出SpaER方法，通过等变神经网络与自注意力机制学习时空表示，首次在无数据增强条件下实现胎儿脑部刚性运动的动态跟踪与校正。
摘要：
本文提出了SpaER，这是一种开创性的胎儿运动跟踪方法，利用等变滤波器和自注意力机制来有效学习时空表示。与传统的从图像对中静态估计胎儿脑运动的方法不同，我们的方法在时间和空间维度上动态跟踪胎儿头部的刚性运动模式。具体而言，我们首先开发了一个等变神经网络，通过图像的低维空间表示高效地学习刚性运动序列。随后，我们通过引入时间编码和自注意力神经网络层来学习时空表示。该方法能够捕获胎儿脑运动的长期依赖关系，并解决由于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一种几何变形估计，能够恰当地处理所有时间帧之间的图像畸变。据我们所知，我们的方法是首个在不使用数据增强的情况下，通过深度神经网络学习时空表示来进行胎儿运动跟踪的方法。我们使用带有模拟运动和真实运动的真实胎儿回波平面图像验证了模型。我们的方法在胎儿MRI序列中准确测量、跟踪和校正胎儿运动方面具有重要的潜在价值。
英文摘要：
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.
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