Longitudinal brain atlases that present brain development trend along time, are essential tools for brain development studies. However, conventional methods construct these atlases by independently averaging brain images from different individuals at discrete time points. This approach could introduce temporal inconsistencies due to variations in ontogenetic trends among samples, potentially affecting accuracy of brain developmental characteristic analysis. In this paper, we propose an implicit neural representation (INR)-based framework to improve the temporal consistency in longitudinal atlases. We treat temporal inconsistency as a 4-dimensional (4D) image denoising task, where the data consists of 3D spatial information and 1D temporal progression. We formulate the longitudinal atlas as an implicit function of the spatial–temporal coordinates, allowing structural inconsistency over the time to be considered as 3D image noise along age. Inspired by recent self-supervised denoising methods (e.g. Noise2Noise), our approach learns the noise-free and temporally continuous implicit function from inconsistent longitudinal atlas data. Finally, the time-consistent longitudinal brain atlas can be reconstructed by evaluating the denoised 4D INR function at critical brain developing time points. We evaluate our approach on three longitudinal brain atlases of different MRI modalities, demonstrating that our method significantly improves temporal consistency while accurately preserving brain structures. Additionally, the continuous functions generated by our method enable the creation of 4D atlases with higher spatial and temporal resolution. Code: https://github.com/maopaom/COLLATOR.

中文翻译：

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COLLATOR：通过隐式神经表示构建一致的时空纵向图集


纵向脑图谱呈现大脑发育趋势，是大脑发育研究的重要工具。然而，传统方法通过在离散时间点独立平均来自不同个体的大脑图像来构建这些图谱。由于样本之间个体发育趋势的变化，这种方法可能会引入时间不一致，从而可能影响大脑发育特征分析的准确性。在本文中，我们提出了一个基于内隐神经表示 （INR） 的框架，以提高纵向图集的时间一致性。我们将时间不一致视为 4 维 （4D） 图像去噪任务，其中数据由 3D 空间信息和 1D 时间进程组成。我们将纵向图谱表述为时空坐标的隐含函数，允许将随时间推移的结构不一致视为随年龄增长的 3D 图像噪声。受最近的自监督降噪方法（例如 Noise2Noise）的启发，我们的方法从不一致的纵向图集数据中学习无噪声和时间连续的隐式函数。最后，可以通过在关键的大脑发育时间点评估去噪的 4D INR 功能来重建时间一致的纵向脑图谱。我们在不同 MRI 模式的三个纵向脑图谱上评估了我们的方法，证明我们的方法显着提高了时间一致性，同时准确地保留了大脑结构。此外，我们的方法生成的连续函数可以创建具有更高空间和时间分辨率的 4D 图集。代码：https://github.com/maopaom/COLLATOR。