Department of Radiology, New York University School of Medicine, New York, NY, USA.
Neuroimage. 2011 Sep 1;58(1):177-88. doi: 10.1016/j.neuroimage.2011.06.006. Epub 2011 Jun 13.
Diffusional kurtosis imaging (DKI) is a clinically feasible extension of diffusion tensor imaging that probes restricted water diffusion in biological tissues using magnetic resonance imaging. Here we provide a physically meaningful interpretation of DKI metrics in white matter regions consisting of more or less parallel aligned fiber bundles by modeling the tissue as two non-exchanging compartments, the intra-axonal space and extra-axonal space. For the b-values typically used in DKI, the diffusion in each compartment is assumed to be anisotropic Gaussian and characterized by a diffusion tensor. The principal parameters of interest for the model include the intra- and extra-axonal diffusion tensors, the axonal water fraction and the tortuosity of the extra-axonal space. A key feature is that these can be determined directly from the diffusion metrics conventionally obtained with DKI. For three healthy young adults, the model parameters are estimated from the DKI metrics and shown to be consistent with literature values. In addition, as a partial validation of this DKI-based approach, we demonstrate good agreement between the DKI-derived axonal water fraction and the slow diffusion water fraction obtained from standard biexponential fitting to high b-value diffusion data. Combining the proposed WM model with DKI provides a convenient method for the clinical assessment of white matter in health and disease and could potentially provide important information on neurodegenerative disorders.
扩散峰度成像(DKI)是扩散张量成像的一种临床可行的扩展,它利用磁共振成像来探测生物组织中受限的水分子扩散。在这里,我们通过将组织建模为两个不可交换的隔室,即轴内空间和轴外空间,为白质区域的 DKI 指标提供了一种具有物理意义的解释。对于 DKI 中常用的 b 值,假设每个隔室中的扩散是各向异性的高斯分布,并由扩散张量来描述。该模型的主要感兴趣参数包括轴内和轴外扩散张量、轴内水分数和轴外空间的迂曲度。一个关键的特点是,这些参数可以直接从 DKI 常规获得的扩散指标中确定。对于三名健康的年轻人,我们从 DKI 指标中估计了模型参数,并发现它们与文献值一致。此外,作为对这种基于 DKI 的方法的部分验证,我们证明了 DKI 衍生的轴内水分数与从高 b 值扩散数据的标准双指数拟合获得的缓慢扩散水分数之间存在良好的一致性。将所提出的 WM 模型与 DKI 相结合,为评估健康和疾病状态下的白质提供了一种便捷的方法,并且可能为神经退行性疾病提供重要信息。