Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy.
Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Neuroimage. 2022 Jul 1;254:119137. doi: 10.1016/j.neuroimage.2022.119137. Epub 2022 Mar 23.
Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-Gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (K), variance due to diffusion anisotropy (K), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modelling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in grey and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple Gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.
扩散磁共振成像（dMRI）已成为非侵入性探测组织微观结构的最重要成像方式之一。扩散峰度磁共振成像（DKI）量化了非高斯扩散的程度，这反过来又被证明可以提高对神经组织中疾病和方向映射等的敏感性。然而，DKI 的特异性有限，因为不同的来源可能会导致总体体素内扩散峰度增加，包括：扩散张量幅度的方差（K）、扩散各向异性引起的方差（K）以及与受限扩散、微结构紊乱和/或交换相关的微观峰度（μK）。有趣的是，μK 在扩散磁共振成像信号建模中通常被忽略，因为它被假设在神经组织中可以忽略不计。然而，最近，基于双扩散编码（DDE）的相关张量磁共振成像（CTI）被引入用于峰度源分离，揭示了临床前成像中μK 的非零值。在这里，我们首次在临床 3T 扫描仪上实现了 CTI，并研究了健康受试者中总峰度的来源。引入了一种用于人体峰度源分离的稳健框架，随后估计了健康大脑中的μK（和其他峰度源）。使用这种临床 CTI 方法，我们发现μK 在灰质和白质组织中对总扩散峰度有显著贡献，但正如预期的那样，在脑室中没有贡献。呈现了人类大脑的第一批μK 图，揭示了μK 的空间分布提供了一种独特的对比源，与各向同性和各向异性峰度对应物不同。此外，首次生成了这些峰度源的群组平均模板，这证实了我们在个体水平地图上的发现。我们进一步表明，忽略μK 并假设为峰度源估计的多个高斯分量逼近会导致对其他峰度源的估计产生显著偏差，甚至更糟糕的是，会影响它们的解释。最后，讨论了在潜在的未来临床应用中 CTI 的两倍加速。我们得出结论，CTI 具有在健康和病理组织中进行未来体内微观结构特征描述的巨大潜力。
