White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.

中文翻译：

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Entrack：用于纤维束成像的熵正则化的概率球面回归

基于扩散加权磁共振图像的白质纤维束成像是目前唯一可用的体内方法来收集有关结构性大脑连接的信息。扩散 MRI 数据的低分辨率建议采用概率方法进行流线重建，即用于纤维交叉。我们提出了一种基于 Fisher-von-Mises 分布的球形回归通用概率模型，该模型使用机器学习方法有效地估计了局部流线方向的最大熵后验。流线后验的最佳精度由信息论技术、预期的对数后验一致性概念决定。它依赖于流线的后验分布的要求，根据同一主题的重新测试测量推断，