Traditional diffusion tensor imaging (DTI) maps brain structure by fitting a diffusion model to the magnitude of the electrical signal acquired in magnetic resonance imaging (MRI). Fractional DTI employs anomalous diffusion models to obtain a better fit to real MRI data, which can exhibit anomalous diffusion in both time and space. In this paper, we describe the challenge of developing and employing anisotropic fractional diffusion models for DTI. Since anisotropy is clearly present in the three-dimensional MRI signal response, such models hold great promise for improving brain imaging. We then propose some candidate models, based on stochastic theory.

中文翻译：

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各向异性分数扩散张量成像。

传统的扩散张量成像（DTI）通过将扩散模型拟合到磁共振成像（MRI）中获取的电信号的大小来映射大脑结构。分数DTI采用异常扩散模型来更好地拟合实际MRI数据，该数据可以在时间和空间上表现出异常扩散。在本文中，我们描述了为DTI开发和采用各向异性分数扩散模型的挑战。由于三维MRI信号响应中显然存在各向异性，因此此类模型对改善脑部成像具有广阔的前景。然后，我们基于随机理论提出一些候选模型。