We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimation methods remain time-intensive. In this preliminary work, we frame subject-specific atlas building as learning a neural field of deformable spatiotemporal observations. We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero. Our method yields high-quality atlases of fetal BOLD time-series with $\sim$5-7$\times$ faster convergence compared to existing work. While our method slightly underperforms well-tuned baselines in terms of anatomical overlap, it estimates templates significantly faster, thus enabling rapid processing and stabilization of large databases of 4D dynamic MRI acquisitions. Code is available at https://github.com/Kidrauh/neural-atlasing

利用神经场快速构建生物医学图像图谱的方法；将个体特定图谱的构建视为学习可变形时空观测的神经场；将该方法应用于胎儿子宫内动态BOLD MRI时间序列的学习和运动稳定化；与现有方法相比，我们的方法收敛速度快5-7倍，为胎儿BOLD时间序列提供高质量的图谱，虽然在解剖重叠方面略低于优化的基准模型，但在模板估计方面显著提高了速度，从而使得大型数据库的4D动态MRI采集能够快速处理和稳定。

学习4D胎儿磁共振时间序列的动态神经场