COLLATOR:通过隐性神经表现来构建一致的时空纵向地图集
Lixuan Chen1, Xuanyu Tian2, Jiangjie Wu2
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Medical image analysis
|December 6, 2024
概括
这项研究引入了一种新的隐性神经表示 (INR) 框架,以增强纵向大脑图谱中的时间一致性. 该方法将不一致性视为噪声,从而创建更准确的发展趋势分析.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 医学图像分析 医学图像分析
背景情况:
- 纵向大脑地图对于研究随时间推移的大脑发育至关重要.
- 传统的地图集构造方法平均图像在离散的时间点,导致时间不一致,影响发育分析的准确性.
研究的目的:
- 提出基于隐式神经表示 (INR) 的框架,以改善纵向大脑图集的时间一致性.
- 解决大脑发育特征分析中的不准确性,这是由于现有地图书中的时间不一致造成的.
主要方法:
- 制定了纵向地图结构作为一个4D (空间-时间) 图像无色化任务.
- 将时间不一致性作为空间时间坐标隐性函数中的噪声来处理.
- 采用由Noise2Noise启发的自我监督的denoising方法,从不一致的数据中学习一个时间连续的隐性函数.
主要成果:
- 在不同MRI模式的纵向大脑图谱中显著改善了时间一致性.
- 精确保存大脑结构,同时增强时间连续性.
- 通过连续的功能生成,可以创建具有更高空间和时间分辨率的4D地图集.
结论:
- 拟议的基于INR的框架有效地提高了纵向大脑地图的时间一致性.
- 这种方法为分析大脑发育趋势提供了更准确的方法.
- 产生的地图集的连续性允许使用更高分辨率的发育建模.
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